{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "source": [
    "\n",
    "# Introduction to input-output analysis using Python\n",
    "\n",
    "Author: <a href=\"mailto:a.owen@leeds.ac.uk\"> Dr Anne Owen </a> \n",
    "\n",
    "The general structure of an input-output table is:\n",
    "\n",
    "\n",
    "<img src=\"https://user-images.githubusercontent.com/24877051/146377849-723d8122-7e2a-4d0b-b45c-f8351c4fcbb8.png\" width=400 height=400 />\n",
    "\n",
    "\n",
    "Consider this very simple model of the economies of <a href=\"https://parksandrecreation.fandom.com/wiki/Pawnee,_Indiana\"> the town of Pawnee</a> and <a href=\"https://parksandrecreation.fandom.com/wiki/Eagleton,_Indiana\"> the town of Eagleton</a><sup>*</sup>:\n",
    "\n",
    "<img src=\"https://github.com/earao/images/blob/main/Picture%201.png?raw=true\" width=600 height=470 />\n",
    "\n",
    "We are going to use the above data to find the GHG emissions for:\n",
    "\n",
    "<ul>\n",
    "  <li>Eagleton's Production</li>\n",
    "  <li>Pawnee's Production</li>\n",
    "  <li>Eagleton's Consumption</li>\n",
    "  <li>Pawnee's Consumption</li>\n",
    "</ul>\n",
    "\n",
    "\n",
    "But, before we start, paste the following into the box below:\n",
    "\n",
    "```python\n",
    "import matplotlib as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
    "%precision 2\n",
    "```\n",
    "\n",
    "<sup>*</sup>Knowledge of <a href=\"https://parksandrecreation.fandom.com/wiki/Main_Page\"> the TV show Parks & Recreation</a> not required for this module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'%.2f'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
    "%precision 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Constructing the dataset in Python\n",
    "\n",
    "## Exercise 1.1: Make arrays for Z,Y and f\n",
    "\n",
    "In Python use the following code to make the array $\\textbf{Z}$\n",
    "\n",
    "```python\n",
    "Z_data = np.array([[660,10,2,30,1,1],\n",
    "                   [300,500,100,20,30,20],\n",
    "                   [200,400,300,10,30,100],\n",
    "                   [40,2,1,440,20,2],\n",
    "                   [20,200,200,140,400,90],\n",
    "                   [10,30,200,200,200,200]])\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[660,  10,   2,  30,   1,   1],\n",
       "       [300, 500, 100,  20,  30,  20],\n",
       "       [200, 400, 300,  10,  30, 100],\n",
       "       [ 40,   2,   1, 440,  20,   2],\n",
       "       [ 20, 200, 200, 140, 400,  90],\n",
       "       [ 10,  30, 200, 200, 200, 200]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z_data = np.array([[660,10,2,30,1,1],\n",
    "                   [300,500,100,20,30,20],\n",
    "                   [200,400,300,10,30,100],\n",
    "                   [40,2,1,440,20,2],\n",
    "                   [20,200,200,140,400,90],\n",
    "                   [10,30,200,200,200,200]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the same format to make $\\textbf{Y}$ and call the variable ```Y_data```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[690,  60],\n",
       "       [380,  20],\n",
       "       [330,  15],\n",
       "       [200, 580],\n",
       "       [ 90, 200],\n",
       "       [100, 280]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_data = np.array([[690,60],\n",
    "                   [380,20],\n",
    "                   [330,15],\n",
    "                   [200,580],\n",
    "                   [90,200],\n",
    "                   [100,280]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and make $\\textbf{f}$, calling the variable ```f_data```. Remember that the format for making a row vector is ```np.array([[number, number, number]])``` with TWO sets of square brackets!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 980, 1000, 1200, 1000, 1100, 1550]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_data = np.array([[980,1000,1200,1000,1100,1550]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to make use of the Pandas library in Python. Pandas is excellent for handling databases. We are going to turn our array data into dataframes which can include the row and column headings. We can also use more calculation properties available with dataframes.\n",
    "\n",
    "## Exercise 1.2: Make heading files\n",
    "\n",
    "Copy the following into the box below:\n",
    "\n",
    "```python\n",
    "sectors = ['Eagleton Food',\n",
    "           'Eagleton Machines',\n",
    "           'Eagleton Energy',\n",
    "           'Pawnee Food',\n",
    "           'Pawnee Machines',\n",
    "           'Pawnee Energy']\n",
    "```\n",
    " \n",
    " to make a heading file for the columns and rows of $\\textbf{Z}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Eagleton Food',\n",
       " 'Eagleton Machines',\n",
       " 'Eagleton Energy',\n",
       " 'Pawnee Food',\n",
       " 'Pawnee Machines',\n",
       " 'Pawnee Energy']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sectors = ['Eagleton Food',\n",
    "           'Eagleton Machines',\n",
    "           'Eagleton Energy',\n",
    "           'Pawnee Food',\n",
    "           'Pawnee Machines',\n",
    "           'Pawnee Energy']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now make another heading file called 'final_demand' which contains the headings 'Eagleton Final Demand' and 'Pawnee Final Demand'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Eagleton Final Demand', 'Pawnee Final Demand']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_demand = ['Eagleton Final Demand',\n",
    "               'Pawnee Final Demand']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 1.3: Make dataframes for Z,Y and f\n",
    "\n",
    "We are going to use the pandas DataFrame function to convert our data into a more useful format.\n",
    "\n",
    "If you take a look at the notation for this function (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html) it says:\n",
    "\n",
    "class pandas.DataFrame(data=None, index=None, columns=None, dtype=None, copy=False)\n",
    "\n",
    "Try\n",
    "\n",
    "```python\n",
    "Z = pd.DataFrame(Z_data, index=sectors, columns=sectors)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Eagleton Food</th>\n",
       "      <th>Eagleton Machines</th>\n",
       "      <th>Eagleton Energy</th>\n",
       "      <th>Pawnee Food</th>\n",
       "      <th>Pawnee Machines</th>\n",
       "      <th>Pawnee Energy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Eagleton Food</th>\n",
       "      <td>660</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eagleton Machines</th>\n",
       "      <td>300</td>\n",
       "      <td>500</td>\n",
       "      <td>100</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eagleton Energy</th>\n",
       "      <td>200</td>\n",
       "      <td>400</td>\n",
       "      <td>300</td>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pawnee Food</th>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>440</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pawnee Machines</th>\n",
       "      <td>20</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>140</td>\n",
       "      <td>400</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pawnee Energy</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Eagleton Food  Eagleton Machines  Eagleton Energy  \\\n",
       "Eagleton Food                660                 10                2   \n",
       "Eagleton Machines            300                500              100   \n",
       "Eagleton Energy              200                400              300   \n",
       "Pawnee Food                   40                  2                1   \n",
       "Pawnee Machines               20                200              200   \n",
       "Pawnee Energy                 10                 30              200   \n",
       "\n",
       "                   Pawnee Food  Pawnee Machines  Pawnee Energy  \n",
       "Eagleton Food               30                1              1  \n",
       "Eagleton Machines           20               30             20  \n",
       "Eagleton Energy             10               30            100  \n",
       "Pawnee Food                440               20              2  \n",
       "Pawnee Machines            140              400             90  \n",
       "Pawnee Energy              200              200            200  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z = pd.DataFrame(Z_data, index=sectors, columns=sectors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are telling Python to make a dataframe, where the data is ```Z_data```, the index (the row headings) is ```sectors``` and the columns is ```sectors```\n",
    "\n",
    "Use this format to make ```Y``` the dataframe for final demand data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Eagleton Final Demand</th>\n",
       "      <th>Pawnee Final Demand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Eagleton Food</th>\n",
       "      <td>690</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eagleton Machines</th>\n",
       "      <td>380</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eagleton Energy</th>\n",
       "      <td>330</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pawnee Food</th>\n",
       "      <td>200</td>\n",
       "      <td>580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pawnee Machines</th>\n",
       "      <td>90</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pawnee Energy</th>\n",
       "      <td>100</td>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Eagleton Final Demand  Pawnee Final Demand\n",
       "Eagleton Food                        690                   60\n",
       "Eagleton Machines                    380                   20\n",
       "Eagleton Energy                      330                   15\n",
       "Pawnee Food                          200                  580\n",
       "Pawnee Machines                       90                  200\n",
       "Pawnee Energy                        100                  280"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = pd.DataFrame(Y_data, index=sectors, columns=final_demand)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Paste the following in to the box below to make a dataframes for  ```f```\n",
    "\n",
    "```f = pd.DataFrame(f_data, columns=sectors)```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Eagleton Food</th>\n",
       "      <th>Eagleton Machines</th>\n",
       "      <th>Eagleton Energy</th>\n",
       "      <th>Pawnee Food</th>\n",
       "      <th>Pawnee Machines</th>\n",
       "      <th>Pawnee Energy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>980</td>\n",
       "      <td>1000</td>\n",
       "      <td>1200</td>\n",
       "      <td>1000</td>\n",
       "      <td>1100</td>\n",
       "      <td>1550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Eagleton Food  Eagleton Machines  Eagleton Energy  Pawnee Food  \\\n",
       "0            980               1000             1200         1000   \n",
       "\n",
       "   Pawnee Machines  Pawnee Energy  \n",
       "0             1100           1550  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = pd.DataFrame(f_data, columns=sectors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dont worry about the zero as the index here - it doesn't really matter.\n",
    "\n",
    "## Exercise 1.4: Referring to DataFrame items\n",
    "\n",
    "When we worked with arrays, we could write ```Z_data[2,3]``` to refer to the item in the 3rd row down and 4th column accross (remember Python counts from zero). This value represents the monetary inputs of Eagleton's Energy to making a Pawnee Food product.\n",
    "\n",
    "With DataFrames we write:\n",
    "```python\n",
    "Z.iloc[2,3]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z.iloc[2,3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that ```iloc``` refers to a numerical position.\n",
    "\n",
    "We can also use the headings to refer to a cell. \n",
    "\n",
    "Try \n",
    "```python\n",
    "Z.loc['Eagleton Energy','Pawnee Food']\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z.loc['Eagleton Energy','Pawnee Food']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that ```loc``` refers to a position by name.\n",
    "\n",
    "Now if you wanted to extract the complete data of where Eagleton's energy goes to (the Pawnee energy row), you can use:\n",
    "```python\n",
    "Z.loc['Eagleton Energy']\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Eagleton Food        200\n",
       "Eagleton Machines    400\n",
       "Eagleton Energy      300\n",
       "Pawnee Food           10\n",
       "Pawnee Machines       30\n",
       "Pawnee Energy        100\n",
       "Name: Eagleton Energy, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z.loc['Eagleton Energy']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To extract the column of data showing inputs to Pawnee's Food (the production recipe of Pawnee's food), you can use:\n",
    "```python\n",
    "Z['Pawnee Food']\n",
    "```\n",
    "\n",
    "Extracting or referring to columns does not require ```loc```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Eagleton Food         30\n",
       "Eagleton Machines     20\n",
       "Eagleton Energy       10\n",
       "Pawnee Food          440\n",
       "Pawnee Machines      140\n",
       "Pawnee Energy        200\n",
       "Name: Pawnee Food, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z['Pawnee Food']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 1.5: Make a vector of total output x\n",
    "\n",
    "Calculate the column vector of total output. You will need to sum along the rows of both $\\textbf{Z}$ and $\\textbf{Y}$. We can use ```np.sum('data','dimension')```. Call your variable $\\textbf{x}$\n",
    "\n",
    "Try \n",
    "```python\n",
    "x = np.sum(Z,1) + np.sum(Y,1)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Eagleton Food        1454\n",
       "Eagleton Machines    1370\n",
       "Eagleton Energy      1385\n",
       "Pawnee Food          1285\n",
       "Pawnee Machines      1340\n",
       "Pawnee Energy        1220\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.sum(Z,1) + np.sum(Y,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scroll back up to the top to check that you have the correct total output figures from the original table.\n",
    "\n",
    "Lets make it neat by putting it in a dataframe:\n",
    "\n",
    "```x = pd.DataFrame(x,index=sectors)```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Eagleton Food</th>\n",
       "      <td>1454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eagleton Machines</th>\n",
       "      <td>1370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eagleton Energy</th>\n",
       "      <td>1385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pawnee Food</th>\n",
       "      <td>1285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pawnee Machines</th>\n",
       "      <td>1340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pawnee Energy</th>\n",
       "      <td>1220</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      0\n",
       "Eagleton Food      1454\n",
       "Eagleton Machines  1370\n",
       "Eagleton Energy    1385\n",
       "Pawnee Food        1285\n",
       "Pawnee Machines    1340\n",
       "Pawnee Energy      1220"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = pd.DataFrame(x,index=sectors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that we can use the numpy function 'sum' with DataFrames. The result $\\textbf{x}$ keeps the row headings because it has checked that both $\\textbf{Z}$ and $\\textbf{Y}$ contain the same sectors\n",
    "\n",
    "In input-output analysis the sum of the rows is the same as the sum of the columns so we could have added the columns of $\\textbf{Z}$ to the value added data. It is best practise to sum along $\\textbf{Z}$ and $\\textbf{Y}$ however becuase sometimes IO tables do not balance and this data is considered more accurate.\n",
    "\n",
    "## Exercise 1.6: Finding total production emissions in Eagleton and Pawnee\n",
    "\n",
    "Can you calculate the total production emissions for Eagleton?\n",
    "\n",
    "The emissions are held in the emissions vector $\\textbf{f}$ and we can either sum the emissions by referring to their position in the vector or by naming the start and end sectors. Here we describe selecting all the rows and the columns between the appropriate headings\n",
    "\n",
    "Try \n",
    "```python\n",
    "eagleton_production = np.sum(f.loc[:,'Eagleton Food':'Eagleton Energy'], 1)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3180\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eagleton_production = np.sum(f.loc[:,'Eagleton Food':'Eagleton Energy'], 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or, we can  refering to the locations in numeric form. Python starts to count from zero and to refer to a range of cells, you tell Python the start cell and cell after the one you want to end on.\n",
    "\n",
    "So we would write:\n",
    "\n",
    "```python\n",
    "eagleton_production = np.sum(f.iloc[:,0:3],1)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3180\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eagleton_production = np.sum(f.iloc[:,0:3],1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the sum of Pawnee's production emissions by referring to the cell labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3650\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pawnee_production = np.sum(f.loc[:,'Pawnee Food':'Pawnee Energy'], 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now try and use the cell locations in numeric form:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3650\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pawnee_production = np.sum(f.iloc[:,3:6],1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Make the A matrix \n",
    "\n",
    "Now, we want to calculate the emissions from consumption rather than production. This means that we need to be able to calculate emissions $\\textbf{f}$ as a function of final demand $\\textbf{Y}$.\n",
    "\n",
    "We start by formulating output $\\textbf{x}$ as a function of final demand $\\textbf{Y}$.\n",
    "\n",
    "We know that $\\textbf{x}$ = $\\textbf{Z}$+$\\textbf{Y}$\n",
    "\n",
    "Which, for a general row $i$ in our matrix, can be written:\n",
    "\n",
    "$x_i$ = $z_{i1}$ + $z_{i2}$ + $z_{i3}$ + $z_{i4}$ + $z_{i5}$ + $z_{i6}$ + $y_{i1}$ + $y_{i2}$ \n",
    "\n",
    "Now we produce a matrix $\\textbf{A}$ which shows the contribution each element in $\\textbf{Z}$ makes towards total output $\\textbf{x}$. Essentially this means dividing each column in $\\textbf{Z}$ by the matching element in $\\textbf{x}$.\n",
    "\n",
    "Now:\n",
    "\n",
    "$a_{ij}$ = $\\frac{z_{ij}}{x_j}$\n",
    "\n",
    "So we can write:\n",
    "\n",
    "$x_i$ = $a_{i1}{x_1}$ + $a_{i2}{x_2}$ + $a_{i3}{x_3}$ + $a_{i4}{x_4}$ + $a_{i5}{x_5}$ + $a_{i6}{x_6}$ + $y_{i1}$ + $y_{i2}$ \n",
    "\n",
    "and so\n",
    "\n",
    "$\\textbf{x}$ = $\\textbf{Ax}$+$\\textbf{Y}$\n",
    "\n",
    "$\\textbf{Y}$ = $\\textbf{x}$ - $\\textbf{Ax}$ \n",
    "\n",
    "$\\textbf{x}$ = $(\\textbf{I}$ - $\\textbf{A})^{-1}$ $\\textbf{Y}$\n",
    "\n",
    "or\n",
    "\n",
    "$\\textbf{x}$ = $\\textbf{L}\\textbf{Y}$\n",
    "\n",
    "$\\textbf{L}$ is known as the Leontief inverse and helps us to express output of industry as a function of demand on products. It essentially redistributes output by industry to final demand by product.\n",
    "\n",
    "In order to calculate $\\textbf{L}$ we first have to make $\\textbf{A}$.\n",
    "\n",
    "In Python, the easiest way to do this is an element-wise division where each cell in $\\textbf{Z}$ is divided by a second matrix, which has the same dimensions (size) as $\\textbf{Z}$ but contains $\\textbf{x}$ in row form, repeated down each row.\n",
    "\n",
    "We are going to call this second matrix ```big_X```\n",
    "\n",
    "## Exercise 2.1: Make big_X\n",
    "\n",
    "Try \n",
    "```python\n",
    "big_X = np.tile(np.transpose(x),[6,1])\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1454, 1370, 1385, 1285, 1340, 1220],\n",
       "       [1454, 1370, 1385, 1285, 1340, 1220],\n",
       "       [1454, 1370, 1385, 1285, 1340, 1220],\n",
       "       [1454, 1370, 1385, 1285, 1340, 1220],\n",
       "       [1454, 1370, 1385, 1285, 1340, 1220],\n",
       "       [1454, 1370, 1385, 1285, 1340, 1220]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "big_X = np.tile(np.transpose(x),[6,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can you see how this formula works?\n",
    "\n",
    "We have tranposed $\\textbf{x}$ to make it a row vector, then repeated this 6 times down and once across. We won't turn this into a DataFrame because it is an intermediate step and the row headings would not make sense.\n",
    "\n",
    "## Exercise 2.2: Make A\n",
    "\n",
    "Now try \n",
    "```python\n",
    "A = Z/big_X\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Eagleton Food</th>\n",
       "      <th>Eagleton Machines</th>\n",
       "      <th>Eagleton Energy</th>\n",
       "      <th>Pawnee Food</th>\n",
       "      <th>Pawnee Machines</th>\n",
       "      <th>Pawnee Energy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Eagleton Food</th>\n",
       "      <td>0.453920</td>\n",
       "      <td>0.007299</td>\n",
       "      <td>0.001444</td>\n",
       "      <td>0.023346</td>\n",
       "      <td>0.000746</td>\n",
       "      <td>0.000820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eagleton Machines</th>\n",
       "      <td>0.206327</td>\n",
       "      <td>0.364964</td>\n",
       "      <td>0.072202</td>\n",
       "      <td>0.015564</td>\n",
       "      <td>0.022388</td>\n",
       "      <td>0.016393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eagleton Energy</th>\n",
       "      <td>0.137552</td>\n",
       "      <td>0.291971</td>\n",
       "      <td>0.216606</td>\n",
       "      <td>0.007782</td>\n",
       "      <td>0.022388</td>\n",
       "      <td>0.081967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pawnee Food</th>\n",
       "      <td>0.027510</td>\n",
       "      <td>0.001460</td>\n",
       "      <td>0.000722</td>\n",
       "      <td>0.342412</td>\n",
       "      <td>0.014925</td>\n",
       "      <td>0.001639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pawnee Machines</th>\n",
       "      <td>0.013755</td>\n",
       "      <td>0.145985</td>\n",
       "      <td>0.144404</td>\n",
       "      <td>0.108949</td>\n",
       "      <td>0.298507</td>\n",
       "      <td>0.073770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pawnee Energy</th>\n",
       "      <td>0.006878</td>\n",
       "      <td>0.021898</td>\n",
       "      <td>0.144404</td>\n",
       "      <td>0.155642</td>\n",
       "      <td>0.149254</td>\n",
       "      <td>0.163934</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Eagleton Food  Eagleton Machines  Eagleton Energy  \\\n",
       "Eagleton Food           0.453920           0.007299         0.001444   \n",
       "Eagleton Machines       0.206327           0.364964         0.072202   \n",
       "Eagleton Energy         0.137552           0.291971         0.216606   \n",
       "Pawnee Food             0.027510           0.001460         0.000722   \n",
       "Pawnee Machines         0.013755           0.145985         0.144404   \n",
       "Pawnee Energy           0.006878           0.021898         0.144404   \n",
       "\n",
       "                   Pawnee Food  Pawnee Machines  Pawnee Energy  \n",
       "Eagleton Food         0.023346         0.000746       0.000820  \n",
       "Eagleton Machines     0.015564         0.022388       0.016393  \n",
       "Eagleton Energy       0.007782         0.022388       0.081967  \n",
       "Pawnee Food           0.342412         0.014925       0.001639  \n",
       "Pawnee Machines       0.108949         0.298507       0.073770  \n",
       "Pawnee Energy         0.155642         0.149254       0.163934  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = Z/big_X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that every cell in $\\textbf{A}$ is a number smaller than one. Python is writing these numbers in standard form.\n",
    "\n",
    "The first cell should be '4.54e-01' or 0.454.\n",
    "\n",
    "# 3. Calculating L - the Leontief inverse\n",
    "\n",
    "Leontief's equation is $L = (I-A)^{-1}$\n",
    "\n",
    "## Exercise 3.1: Make the identity matrix I\n",
    "\n",
    "Look back at the matrix algebra workbook and make an identity matrix $\\textbf{I}$ which is 6 cells wide and long"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 0., 0.],\n",
       "       [0., 1., 0., 0., 0., 0.],\n",
       "       [0., 0., 1., 0., 0., 0.],\n",
       "       [0., 0., 0., 1., 0., 0.],\n",
       "       [0., 0., 0., 0., 1., 0.],\n",
       "       [0., 0., 0., 0., 0., 1.]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I = np.identity(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 3.2: Make L\n",
    "\n",
    "Now try to make $\\textbf{L}$ using Python's ```np.linalg.inv``` function that we also saw in the homework"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.85, 0.03, 0.01, 0.07, 0.01, 0.  ],\n",
       "       [0.69, 1.69, 0.18, 0.09, 0.07, 0.06],\n",
       "       [0.61, 0.67, 1.39, 0.11, 0.1 , 0.16],\n",
       "       [0.09, 0.02, 0.01, 1.53, 0.04, 0.01],\n",
       "       [0.34, 0.52, 0.36, 0.32, 1.5 , 0.18],\n",
       "       [0.22, 0.26, 0.31, 0.36, 0.29, 1.26]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L = np.linalg.inv(I-A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should have got a result that looks like this:\n",
    "\n",
    "```python\n",
    "array([[ 1.85,  0.03,  0.01,  0.07,  0.01,  0.  ],\n",
    "       [ 0.69,  1.69,  0.18,  0.09,  0.07,  0.06],\n",
    "       [ 0.61,  0.67,  1.39,  0.11,  0.1 ,  0.16],\n",
    "       [ 0.09,  0.02,  0.01,  1.53,  0.04,  0.01],\n",
    "       [ 0.34,  0.52,  0.36,  0.32,  1.5 ,  0.18],\n",
    "       [ 0.22,  0.26,  0.31,  0.36,  0.29,  1.26]])```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Calculating the consumption emissions for Eagleton and Pawnee\n",
    "\n",
    "Now let $\\textbf{e}$ be a vector of emissions intensity - the tonnes of $CO_2$ per unit of output.\n",
    "\n",
    "$\\textbf{e = fx}^{-1}$\n",
    "\n",
    "multiplying both sides of the Leontief equation by $\\textbf{e}$ gives:\n",
    "\n",
    "$\\textbf{ex} = \\textbf{eLY}$\n",
    "\n",
    "which simplifies to:\n",
    "\n",
    "$\\textbf{f} = \\textbf{eLY}$\n",
    "\n",
    "total emissions can be formulated by the product of emissions intensity, the Leontief inverse and final demand. We have emissions as a function of final demand!\n",
    "\n",
    "We are going to reserve $\\textbf{f}$ for the production vector and use $\\textbf{q}$ for consumption emissions.\n",
    "\n",
    "## Exercise 4.1: Calculating e\n",
    "\n",
    "We cannot  use ```e = f/x``` here because $\\textbf{f}$ is a row vector and $\\textbf{x}$ is a column vector. For this project, we want $\\textbf{e}$ to be a row vector so we must first transpose $\\textbf{x}$. Try:\n",
    "\n",
    "```python\n",
    "e = f/np.transpose(x)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Eagleton Food</th>\n",
       "      <th>Eagleton Machines</th>\n",
       "      <th>Eagleton Energy</th>\n",
       "      <th>Pawnee Food</th>\n",
       "      <th>Pawnee Machines</th>\n",
       "      <th>Pawnee Energy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.674003</td>\n",
       "      <td>0.729927</td>\n",
       "      <td>0.866426</td>\n",
       "      <td>0.77821</td>\n",
       "      <td>0.820896</td>\n",
       "      <td>1.270492</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Eagleton Food  Eagleton Machines  Eagleton Energy  Pawnee Food  \\\n",
       "0       0.674003           0.729927         0.866426      0.77821   \n",
       "\n",
       "   Pawnee Machines  Pawnee Energy  \n",
       "0         0.820896       1.270492  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e = f/np.transpose(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets make a barchart of e. Try:\n",
    "\n",
    "```python\n",
    "chart = e.plot(kind='bar')\n",
    "chart.set_ylabel('tonnes CO2 per £')\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'tonnes CO2 per £')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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E1dUVERERMm3PDtmdPHkSK1aswIULF1BYWIiysjKUlJSguLgYurq6uHTpEkaOHCmzvoODg0zYedbff/+NnJwc+Pn5YfLkyUJ7WVkZDA1rDoOFhYW4c+cOHB0dZdodHR3x+++/y7T16tVL+HPbtm0BAHfv3q0x3FQZNmwYpkyZgtOnT2P37t3VXrUB6j4nGRkZ+M9//lPrvp6tsarOu3fvKrzO88eVlpaGq1evIjo6WuhTWVmJiooKZGVlwc3NDR07doSFhQWGDBmCIUOGYOTIkdDR0al1n0REr4smF272798PX19f7N+/H8OGDVN1OY2Krq4uunTpUu2ymzdvYujQoQgICMDSpUvRsmVLJCUlwc/PD0+ePAHw9BeoRCKRWa+2C3sVFRUAng5NvfHGGzLL1NXV66y3un0939asWTO5/lX7rY2GhgYmTJiAxYsX45dffsF3330n10eRc9K8efM69/VsjVV11lVjbcdVUVGBKVOmICgoSG49MzMzaGpq4rfffkNCQgJ+/PFHfPrppwgJCcGvv/5a5/woIqLXgUrn3Dx8+BAZGRnCEEdWVhYyMjKQnZ0NAFiwYAEmTpwo9N+/fz8mTpyIsLAw9O/fH3l5ecjLy0NBgfJ3Fr1uUlNTUVZWJpy7bt264c6dOzJ9LC0thTkyz65XExMTE7Rv3x7Xr19Hly5dZD5VE4k1NTUBQGaOioGBAdq1ayc31JOSkgIrK6uXOs5n+fr64tSpU/Dy8pKZWF1FkXPSq1cv/PTTT/VWkyL69u2LP//8U+6cdunSRTifGhoaePvtt7FmzRqcO3cON27cwIkTJ15pnUREjZVKr9ykpqbC1dVV+B4cHAwAmDRpEqKiopCbmysEHQDYtm0bysrKMG3aNEybNk1or+r/uistLUVeXp5Mm4aGBoyNjdG5c2eUlZVh06ZNGD58OJKTk7F161aZvh9//DGcnJwQHh6O4cOH48SJEzh69Kjc1ZRnhYSEICgoCAYGBvD09ERpaSlSU1Pxzz//IDg4GG3atEHz5s0RFxeHDh06QFtbG4aGhpgzZw4WL16Mzp07w9bWFpGRkcjIyJAZinlZVlZWyM/Pr3G4RpFzsmDBAtjY2CAwMBABAQHQ1NTEyZMn8Z///AfGxsb1Vuuz5s2bh/79+2PatGmYPHkydHV1kZmZifj4eGzatAlHjhzB9evX4eTkhBYtWiA2NhYVFRXo3r17g9RDRNTUqPTKjYuLCyorK+U+VUElKioKCQkJQv+EhIRa+7/u4uLi0LZtW5nPm2++CQCwtbVFeHg4Vq9ejZ49eyI6OhorV66UWd/R0RFbt25FeHg4evfujbi4OMycObPWiaj+/v7YuXMnoqKiYGNjA2dnZ0RFRQlXbjQ0NLBx40Zs27YN7dq1g5eXFwAgKCgIs2bNwqxZs2BjY4O4uDgcPnwYXbt2rddz0qpVqxqHlhQ5J926dcOPP/6I33//HQ4ODhgwYAC+//57uVvK61OvXr1w6tQpXLlyBYMGDUKfPn2waNEiYW6OkZERDh48iLfeegtWVlbYunUr9u/fjx49ejRYTURETUmjuVvqValttjXvLJE3efJkXLx4UeZuIGo6+DNNL4N3S1FjIvq7pajhrF27Fm5ubtDV1cXRo0exZ88emYfyERERNXYMNyTj7NmzWLNmDYqKimBhYYGNGzfC399f1WUREREpjOGGZHz99deqLoGIiOilNOnXLxARERE9j+GGiIiIRIXhhoiIiESF4YaIiIhEheGGiIiIRIXhhoiIiESF4YaUEhISAltbW1WXQUREVCM+50ZBNntsXun+zk86r1R/Hx8f7NmzR67dw8MDcXFx9VWW0m7cuAFzc3Okp6e/klDUqVMn3Lx5U6595cqVmD9/foPvn4iIVI/hRkSGDBmCyMhImTYtLS0VVaM6oaGhmDx5skybvr5+g+7zyZMnaNasWYPug4iIFMNhKRHR0tKCqampzKdFixbC8vDwcNjY2EBXVxdSqRSBgYF4+PChzDZ27NgBqVQKHR0djBw5EuHh4TAyMqp1v5GRkbCysoK2tjYsLS1l3kVV9XbwPn36QCKRwMXFBQBQUVGB0NBQdOjQAVpaWrC1tZW5wnTjxg1IJBIcPHgQrq6u0NHRQe/evXHmzJk6z4O+vr7cedDV1QXw9M3yEokEP/30E+zt7aGjo4OBAwfi0qVLMtv44YcfYGdnB21tbVhYWGDJkiUoKysTlkskEmzduhVeXl7Q1dXFsmXLAADLli1DmzZtoK+vD39/f8yfP1+4YnX69Gk0a9YMeXl5MvuaNWsWnJyc6jwuIiJSDMPNa0RNTQ0bN27EH3/8gT179uDEiROYO3eusDw5ORkBAQGYPn06MjIy4ObmhuXLl9e6zR07dmDhwoVYvnw5MjMzsWLFCixatEgYIjt79iwA4Pjx48jNzcXBgwcBABs2bEBYWBjWrl2Lc+fOwcPDA++++y6uXLkis/2FCxdi9uzZyMjIQLdu3TB27FiZkPGiFi5ciLCwMKSmpkJDQwO+vr7CsmPHjuGDDz5AUFAQLly4gG3btiEqKkruXCxevBheXl44f/48fH19ER0djeXLl2P16tVIS0uDmZkZIiIihP5OTk6wsLDAl19+KbSVlZVh7969+PDDD1/6mIiI6CmGGxE5cuQI9PT0ZD5Lly4Vls+YMQOurq4wNzfHW2+9haVLl8q8S2rTpk3w9PTE7Nmz0a1bNwQGBsLT07PWfS5duhRhYWHw9vaGubk5vL29MXPmTGzbtg0A0Lp1awBAq1atYGpqipYtWwJ4+vbxefPmYcyYMejevTtWr14NW1tbrF+/Xmb7s2fPxrBhw9CtWzcsWbIEN2/exNWrV2utad68eXLnISEhQabP8uXL4ezsDGtra8yfPx8pKSkoKSkRls2fPx+TJk2ChYUF3NzcsHTpUuGYqowbNw6+vr6wsLBAx44dsWnTJvj5+eHDDz9Et27d8Omnn8LGRnaulp+fn8zQ4f/+9z88evQIo0aNqvWYiIhIcZxzIyKurq4yVwoACGECAE6ePIkVK1bgwoULKCwsRFlZGUpKSlBcXAxdXV1cunQJI0eOlFnfwcEBR44cqXZ/f//9N3JycuDn5yczx6WsrAyGhoY11llYWIg7d+7A0dFRpt3R0RG///67TFuvXr2EP7dt2xYAcPfuXVhaWta4/Tlz5sDHx0emrX379gpt18zMDGlpafj1119lrtSUl5ejpKQEjx49go6ODgDA3t5eZpuXLl1CYGCgTJuDgwNOnDghfPfx8cF///tf/Pzzz+jfvz92796NUaNGCcNmRET08hhuRERXVxddunSpdtnNmzcxdOhQBAQEYOnSpWjZsiWSkpLg5+eHJ0+eAAAqKyshkUhk1qusrKxxfxUVFQCeDk298cYbMsvU1dXrrLe6fT3f9uwk3aplVfutibGxcY3nQZHtVlRUYMmSJfD29pZbT1tbW/hzdYGkrvPXpk0bDB8+HJGRkbCwsEBsbKzcVSUiIno5DDevidTUVJSVlSEsLAxqak9HI58dkgIAS0tLYY7Ms+vVxMTEBO3bt8f169cxfvz4avtoamoCeHrlo4qBgQHatWuHpKQkmYm0KSkpcHBwUO7AGkDfvn1x6dKlOgPS87p3746zZ89iwoQJQlt158/f3x9jxoxBhw4d0LlzZ7krWERE9HIYbkSktLRU7k4cDQ0NGBsbo3PnzigrK8OmTZswfPhwJCcnY+vWrTJ9P/74Yzg5OSE8PBzDhw/HiRMncPToUbmrEc8KCQlBUFAQDAwM4OnpidLSUqSmpuKff/5BcHAw2rRpg+bNmyMuLg4dOnSAtrY2DA0NMWfOHCxevBidO3eGra0tIiMjkZGRgejo6Jc+D0VFRXLnQUdHBwYGBgqt/+mnn+Kdd96BVCrFf/7zH6ipqeHcuXM4f/68cFdUdT7++GNMnjwZ9vb2GDhwIGJiYnDu3DlYWFjI9PPw8IChoSGWLVuG0NBQ5Q+QiIhqxQnFIhIXF4e2bdvKfN58800AgK2tLcLDw7F69Wr07NkT0dHRWLlypcz6jo6O2Lp1K8LDw9G7d2/ExcVh5syZMkMxz/P398fOnTsRFRUFGxsbODs7IyoqSrgFXENDAxs3bsS2bdvQrl07eHl5AQCCgoIwa9YszJo1CzY2NoiLi8Phw4fRtWvXlz4Pn376qdx5ePausLp4eHjgyJEjiI+PR79+/dC/f3+Eh4ejY8eOta43fvx4LFiwALNnz0bfvn2RlZUFHx8fufOnpqYGHx8flJeXY+LEiS90jEREVDNJZW2TKkSosLAQhoaGKCgokPs/+ZKSEmRlZcHc3LzWX+ivk8mTJ+PixYtITExUdSlNkpubG0xNTWVu/waente//voLhw8fbtD982eaXkbY6HdUXUKdZsVUf8MDiU9tv7+fx2EpkrF27Vq4ublBV1cXR48exZ49e2Qeykc1e/ToEbZu3QoPDw+oq6tj//79OH78OOLj44U+BQUF+PXXXxEdHY3vv/9ehdUSEYkXww3JOHv2LNasWYOioiJYWFhg48aN8Pf3V3VZTYJEIkFsbCyWLVuG0tJSdO/eHQcOHMDbb78t9PHy8sLZs2cxZcoUuLm5qbBaIiLxYrghGc/fQUWKa968OY4fP15rH972TUTU8DihmIiIiESF4YaIiIhEheGGiIiIRIXhhoiIiESF4YaIiIhEheGGiIiIRIXhhqgOCQkJkEgkePDggapLISIiBfA5NwrKtLR6pfuzupipVH8fHx/s2bMHwNP3OUmlUnh7e2PJkiXQ1dVtiBJV4tnjfNaVK1eUfos3ERGJE8ONiAwZMgSRkZF48uQJEhMT4e/vj+LiYkRERKi6tHpVdZzPat26tYqqISKixobDUiKipaUFU1NTSKVSjBs3DuPHj8ehQ4cAAHv37oW9vT309fVhamqKcePG4e7du8K6dnZ2CAsLE76PGDECGhoaKCwsBADk5eVBIpHg0qVLAIBOnTphxYoV8PX1hb6+PszMzLB9+3aZem7fvo3Ro0ejRYsWaNWqFby8vHDjxg2ZPpGRkbCysoK2tjYsLS0Veo9V1XE++1FXVwcAnDp1Cg4ODtDS0kLbtm0xf/58lJWVCeuWlpYiKCgIbdq0gba2Nt588038+uuvMtuPjY1Ft27d0Lx5c7i6usrVTEREjRvDjYg1b94cT548AQA8fvwYS5cuxe+//45Dhw4hKysLPj4+Ql8XFxfh1QCVlZVITExEixYtkJSUBAA4efIkTE1N0b17d2GdsLAw2NvbIz09HYGBgZg6dSouXrwI4OlLJF1dXaGnp4fTp08jKSkJenp6GDJkCB4/fgwA2LFjBxYuXIjly5cjMzMTK1aswKJFi6oddlLE7du3MXToUPTr1w+///47IiIisGvXLixbtkzoM3fuXBw4cAB79uzBb7/9hi5dusDDwwP3798HAOTk5MDb2xtDhw5FRkYG/P39MX/+/Beqh4iIVIPhRqTOnj2Lffv2YfDgwQAAX19feHp6wsLCAv3798fGjRtx9OhRPHz4EMDTcJOYmIiKigqcO3cO6urqmDBhghB4EhIS4OzsLLOPoUOHIjAwEF26dMG8efNgbGws9P/qq6+gpqaGnTt3wsbGBlZWVoiMjER2drbQZ+nSpQgLC4O3tzfMzc3h7e2NmTNnYtu2bbUe25EjR6Cnpyd8/vOf/wAAtmzZAqlUis8//xyWlpYYMWIElixZgrCwMFRUVAhDdJ999hk8PT1hbW2NHTt2oHnz5ti1axcAICIiAhYWFli3bh26d++O8ePHy4RAIiJq/DjnRkSqfumXlZXhyZMn8PLywqZNmwAA6enpCAkJQUZGBu7fv4+KigoAQHZ2NqytreHk5ISioiKkp6cjOTkZzs7OcHV1Fa56JCQkYMaMGTL769Wrl/BniUQCU1NTYagrLS0NV69ehb6+vsw6JSUluHbtGv7++2/k5OTAz88PkydPFpaXlZXB0NCw1uN0dXWVmUdUNWE6MzMTAwYMgEQiEZY5Ojri4cOHuHXrFh48eIAnT57A0dFRWN6sWTM4ODggMzNT2Eb//v1ltjFgwIBa6yEiosaF4UZEqn7pN2vWDO3atUOzZs0AAMXFxXB3d4e7uzv27t2L1q1bIzs7Gx4eHsIQkaGhIWxtbZGQkICUlBS89dZbGDRoEDIyMnDlyhVcvnwZLi4uMvur2n4ViUQihKaKigrY2dkhOjpars7WrVujpKQEwNOhqTfeeENmedX8mZro6upWe2dUZWWlTCipaquq7dk/17ReVR8iImq6GG5EpKZf+hcvXkR+fj5WrVoFqVQKAEhNTZXr5+LigpMnT+KXX35BaGgojIyMYG1tjWXLlqFNmzawslL8dvi+ffsiJiYGbdq0gYGBgdxyQ0NDtG/fHtevX8f48eOVOMqaWVtb48CBAzJhJSUlBfr6+mjfvj1atmwJTU1NJCUlYdy4cQCAJ0+eIDU1VbgqZW1tLUzCrvLzzz/XS31ERPRqcM7Na8DMzAyamprYtGkTrl+/jsOHD2Pp0qVy/VxcXBAXFweJRAJra2uhLTo6Wm6+TV3Gjx8PY2NjeHl5ITExEVlZWTh16hSmT5+OW7duAQBCQkKwcuVKbNiwAZcvX8b58+cRGRmJ8PDwFzrOwMBA5OTk4OOPP8bFixfx/fffY/HixQgODoaamhp0dXUxdepUzJkzB3Fxcbhw4QImT56MR48ewc/PDwAQEBCAa9euITg4GJcuXcK+ffsQFRX1QvUQEZFqMNy8Blq3bo2oqCh88803sLa2xqpVq7B27Vq5fk5OTgAAZ2dn4cqHs7MzysvLlQ43Ojo6OH36NMzMzODt7Q0rKyv4+vri33//Fa7k+Pv7Y+fOnYiKioKNjQ2cnZ0RFRUFc3PzFzrO9u3bIzY2FmfPnkXv3r0REBAAPz8//Pe//xX6rFq1Cu+99x4mTJiAvn374urVqzh27BhatGgB4GkQPHDgAH744Qf07t0bW7duxYoVK16oHiIiUg1J5Ws2yaCwsBCGhoYoKCiQGy4pKSlBVlYWzM3Noa2traIKieoPf6bpZYSNfkfVJdRpVswRVZdAr0htv7+fxys3REREJCoqDTenT5/G8OHD0a5dO0gkErmJnNU5deoU7OzsoK2tDQsLC2zdurXhCyUiIqImQ6Xhpri4GL1798bnn3+uUP+srCwMHToUgwYNQnp6Oj755BMEBQXhwIEDDVwpERERNRUqvRXc09MTnp6eCvffunUrzMzMsH79egCAlZUVUlNTsXbtWrz33nsNVCURERE1JU1qzs2ZM2fg7u4u0+bh4YHU1FThHUrPKy0tRWFhocyHiIiIxKtJhZu8vDyYmJjItJmYmKCsrAz5+fnVrrNy5UoYGhoKn6qH2BEREZE4NalwA1T/6Pzq2qssWLAABQUFwicnJ6fBayQiIiLVaVKvXzA1NUVeXp5M2927d6GhoYFWrVpVu46Wlha0tLReRXlERETUCDSpKzcDBgxAfHy8TNuPP/4Ie3t7uZc4EhER0etJpeHm4cOHyMjIQEZGBoCnt3pnZGQgOzsbwNMhpYkTJwr9AwICcPPmTQQHByMzMxO7d+/Grl27MHv2bFWUT6+BkJAQ2Nra1trHxcVFePEmERGpnkqHpVJTU+Hq6ip8Dw4OBgBMmjQJUVFRyM3NFYIOAJibmyM2NhYzZ87E5s2b0a5dO2zcuPGV3Aa+OeBEg+/jWdO2vqVUfx8fH+zZswcAoKGhAalUCm9vbyxZsgS6uroNUaJKVB3nlClT5B7gGBgYiIiICOHn51U5ePAgrxwSETUiKg03Li4uqO3VVtX9gnJ2dsZvv/3WgFU1XUOGDEFkZCSePHmCxMRE+Pv7o7i4GBEREaourV5JpVJ89dVXWLduHZo3bw7g6TuU9u/fDzMzs1deT8uWLV/5PomIqGZNas4N1U5LSwumpqaQSqUYN24cxo8fL7zSYu/evbC3t4e+vj5MTU0xbtw43L17V1jXzs4OYWFhwvcRI0ZAQ0NDeC5QXl4eJBIJLl26BADo1KkTVqxYAV9fX+jr68PMzAzbt2+Xqef27dsYPXo0WrRogVatWsHLyws3btyQ6RMZGQkrKytoa2vD0tISW7ZsqfM4+/btCzMzMxw8eFBoO3jwIKRSKfr06SPTNy4uDm+++SaMjIzQqlUrvPPOO7h27ZpMn1u3bmHMmDFo2bIldHV1YW9vj19++UWmz5dffolOnTrB0NAQY8aMQVFRkbDs+WGp+jg3CQkJcHBwgK6uLoyMjODo6IibN2/WeW6IiIjhRtSaN28uPNzw8ePHWLp0KX7//XccOnQIWVlZ8PHxEfq6uLggISEBwNPb6xMTE9GiRQskJSUBAE6ePAlTU1N0795dWCcsLAz29vZIT09HYGAgpk6diosXLwIAHj16BFdXV+jp6eH06dNISkqCnp4ehgwZgsePHwMAduzYgYULF2L58uXIzMzEihUrsGjRImF4rTYffvghIiMjhe+7d++Gr6+vXL/i4mIEBwfj119/xU8//QQ1NTWMHDkSFRUVAJ7O+3J2dsadO3dw+PBh/P7775g7d66wHACuXbuGQ4cO4ciRIzhy5AhOnTqFVatW1Vrfy5ybsrIyjBgxAs7Ozjh37hzOnDmD//u//6vxcQdERCSrSd0KToo7e/Ys9u3bh8GDBwOAzC9+CwsLbNy4EQ4ODnj48CH09PTg4uKCXbt2oaKiAufPn4e6ujo++OADJCQkYOjQoUhISICzs7PMPoYOHYrAwEAAwLx587Bu3TokJCTA0tISX331FdTU1LBz507hl3JkZCSMjIyQkJAAd3d3LF26FGFhYfD29gbwdE7VhQsXsG3bNkyaNKnW45swYQIWLFiAGzduQCKRIDk5GV999ZUQ0Ko8Px9r165daNOmDS5cuICePXti3759+Pvvv/Hrr78Kw0tdunSRWaeiogJRUVHQ19cX9v3TTz9h+fLlNdb3MufG3t4eBQUFeOedd9C5c2cAT181QkREiuGVGxE5cuQI9PT0oK2tjQEDBsDJyQmbNm0CAKSnp8PLywsdO3aEvr4+XFxcAECYsO3k5ISioiKkp6fj1KlTcHZ2hqurK06dOgUA1YabXr16CX+WSCQwNTUVhrrS0tJw9epV6OvrQ09PD3p6emjZsiVKSkpw7do1/P3338jJyYGfn5+wXE9PD8uWLZMbNqqOsbExhg0bhj179iAyMhLDhg2DsbGxXL9r165h3LhxsLCwgIGBAczNzWWOOyMjA3369Kl13kynTp2EYAMAbdu2lRnSq87LnJuWLVvCx8cHHh4eGD58ODZs2IDc3Nw6zwkRET3FKzci4urqioiICDRr1gzt2rUT7uApLi6Gu7s73N3dsXfvXrRu3RrZ2dnw8PAQhogMDQ1ha2uLhIQEpKSk4K233sKgQYOQkZGBK1eu4PLly0IgqvL8HUISiUQYzqmoqICdnR2io6Pl6mzdujVKSkoAPB2aeuONN2SWq6urK3S8vr6++OijjwAAmzdvrrbP8OHDIZVKsWPHDrRr1w4VFRXo2bOncNxVE5JrU9txvsg6dZ0b4OmVnKCgIMTFxSEmJgb//e9/ER8fj/79+9dZLxHR647hRkR0dXXlhlQA4OLFi8jPz8eqVauEd2ulpqbK9XNxccHJkyfxyy+/IDQ0FEZGRrC2tsayZcvQpk0bpYZG+vbti5iYGLRp0wYGBgZyyw0NDdG+fXtcv34d48ePV+Io/79n5+94eHjILb937x4yMzOxbds2DBo0CACEOURVevXqhZ07d+L+/fuv7K6nus5NlT59+qBPnz5YsGABBgwYgH379jHcEBEpgMNSrwEzMzNoampi06ZNuH79Og4fPoylS5fK9XNxcUFcXBwkEgmsra2FtujoaLkhqbqMHz8exsbG8PLyQmJiIrKysnDq1ClMnz4dt27dAvD0AXkrV67Ehg0bcPnyZZw/fx6RkZEIDw9XaB/q6urIzMxEZmZmtVd7qu5E2r59O65evYoTJ04Iz1KqMnbsWJiammLEiBFITk7G9evXceDAAZw5c0ap41VGXecmKysLCxYswJkzZ3Dz5k38+OOPuHz5MufdEBEpiOHmNdC6dWtERUXhm2++gbW1NVatWoW1a9fK9XNycgLw9FlCVRNdnZ2dUV5ernS40dHRwenTp2FmZgZvb29YWVnB19cX//77r3C1wt/fHzt37kRUVBRsbGzg7OyMqKgoYV6MIgwMDGq8+qGmpoavvvoKaWlp6NmzJ2bOnInPPvtMpo+mpiZ+/PFHtGnTBkOHDoWNjQ1WrVql8NDYi6jr3Ojo6ODixYt477330K1bN/zf//0fPvroI0yZMqXBaiIiEhNJZW1P0ROhwsJCGBoaoqCgQO6XYklJCbKysmBubg5tbW0VVUhUf/gzTS8jbPQ7qi6hTrNijqi6BHpFavv9/TxeuSEiIiJRYbghIiIiUWG4ISIiIlFhuCEiIiJRYbghIiIiUWG4ISIiIlFhuCEiIiJRYbghIiIiUWG4ISIiIlFhuCEiIiJR4VvBFfSqH0Ou7CPFfXx8sGfPHgCAhoYGpFIpvL29sWTJEujq6jZEiSrx7HE+y8PDA3FxcSqoiIiIGhuGGxEZMmQIIiMj8eTJEyQmJsLf3x/FxcWIiIhQdWn1quo4n6WlpdWg+3z8+DE0NTUbdB9ERFQ/OCwlIlpaWjA1NYVUKsW4ceMwfvx4HDp0CACwd+9e2NvbQ19fH6amphg3bhzu3r0rrGtnZ4ewsDDh+4gRI6ChoYHCwkIAQF5eHiQSCS5dugQA6NSpE1asWAFfX1/o6+vDzMwM27dvl6nn9u3bGD16NFq0aIFWrVrBy8sLN27ckOkTGRkJKysraGtrw9LSElu2bFH4OJ/9tGjRQlgukUiwc+dOjBw5Ejo6OujatSsOHz4ss40LFy5g6NCh0NPTg4mJCSZMmID8/HxhuYuLCz766CMEBwfD2NgYbm5uAIDDhw+ja9euaN68OVxdXbFnzx5IJBI8ePAAxcXFMDAwwLfffiuzrx9++AG6urooKiqq89iIiOjlMdyIWPPmzfHkyRMAT688LF26FL///jsOHTqErKws+Pj4CH1dXFyQkJAAAKisrERiYiJatGiBpKQkAMDJkydhamqK7t27C+uEhYXB3t4e6enpCAwMxNSpU3Hx4kUAwKNHj+Dq6go9PT2cPn0aSUlJ0NPTw5AhQ/D48WMAwI4dO7Bw4UIsX74cmZmZWLFiBRYtWlTtsJOylixZglGjRuHcuXMYOnQoxo8fj/v37wMAcnNz4ezsDFtbW6SmpiIuLg5//fUXRo0aJbONPXv2QENDA8nJydi2bRtu3LiB999/HyNGjEBGRgamTJmChQsXCv11dXUxZswYuatKkZGReP/996Gvr//Sx0VERHVjuBGps2fPYt++fRg8eDAAwNfXF56enrCwsED//v2xceNGHD16FA8fPgTwNNwkJiaioqIC586dg7q6OiZMmCAEnoSEBDg7O8vsY+jQoQgMDESXLl0wb948GBsbC/2/+uorqKmpYefOnbCxsYGVlRUiIyORnZ0t9Fm6dCnCwsLg7e0Nc3NzeHt7Y+bMmdi2bVutx3bkyBHo6enJfJYuXSrTx8fHB2PHjkWXLl2wYsUKFBcX4+zZswCAiIgI9O3bFytWrIClpSX69OmD3bt34+TJk7h8+bKwjS5dumDNmjXo3r07LC0tsXXrVnTv3h2fffYZunfvjjFjxsgERADw9/fHsWPHcOfOHQBAfn4+jhw5Al9fX8X+wxER0UvjnBsRqfqlX1ZWhidPnsDLywubNm0CAKSnpyMkJAQZGRm4f/8+KioqAADZ2dmwtraGk5MTioqKkJ6ejuTkZDg7O8PV1RXLli0D8DTczJgxQ2Z/vXr1Ev4skUhgamoqDHWlpaXh6tWrclcrSkpKcO3aNfz999/IycmBn58fJk+eLCwvKyuDoaFhrcfp6uoqN4+oZcuWNdamq6sLfX19mdpOnjwJPT09uW1fu3YN3bp1AwDY29vLLLt06RL69esn0+bg4CD3vUePHvjiiy8wf/58fPnllzAzM4OTk1Otx0RERPWH4UZEqn7pN2vWDO3atUOzZs0AAMXFxXB3d4e7uzv27t2L1q1bIzs7Gx4eHsIQkaGhIWxtbZGQkICUlBS89dZbGDRoEDIyMnDlyhVcvnwZLi4uMvur2n4ViUQihKaKigrY2dkhOjpars7WrVujpKQEwNOhqTfeeENmubq6eq3Hqauriy5dutTap67ahg8fjtWrV8ut17ZtW5n9PKuyshISiUSu7Xn+/v74/PPPMX/+fERGRuLDDz+UW4+IiBoOw42I1PRL/+LFi8jPz8eqVasglUoBAKmpqXL9XFxccPLkSfzyyy8IDQ2FkZERrK2tsWzZMrRp0wZWVlYK19K3b1/ExMSgTZs2MDAwkFtuaGiI9u3b4/r16xg/frwSR/ny+vbtiwMHDqBTp07Q0FD8r4ClpSViY2Nl2qo7jx988AHmzp2LjRs34s8//8SkSZNeumYiIlKcwnNuzpw5I0wWrU5xcTFCQ0PrpSiqX2ZmZtDU1MSmTZtw/fp1HD58WG6OCvA03MTFxUEikcDa2lpoi46OlptvU5fx48fD2NgYXl5eSExMRFZWFk6dOoXp06fj1q1bAICQkBCsXLkSGzZswOXLl3H+/HlERkYiPDy81m2XlpYiLy9P5vPsnU51mTZtGu7fv4+xY8fi7NmzuH79On788Uf4+vqivLy8xvWmTJmCixcvYt68ebh8+TK+/vprREVFAYDMlZkWLVrA29sbc+bMgbu7Ozp06KBwbURE9PIUDjdTpkzBX3/9JXzfv38/iouLhe8PHz7EkiVL6rc6qhetW7dGVFQUvvnmG1hbW2PVqlVYu3atXL+qeSHOzs7CL2tnZ2eUl5crHW50dHRw+vRpmJmZwdvbG1ZWVvD19cW///4rXMnx9/fHzp07ERUVBRsbGzg7OyMqKgrm5ua1bjsuLg5t27aV+bz55psK19auXTskJyejvLwcHh4e6NmzJ6ZPnw5DQ0OoqdX8V8Lc3BzffvstDh48iF69eiEiIkK4W+r55+z4+fnh8ePHnEhMRKQCksrqJg1UQ0dHBxcvXoSZmRkAwMDAABkZGbCwsAAA/PXXX2jXrl2t/+fbGBQWFsLQ0BAFBQVywyUlJSXIysqCubk5tLW1VVQhNSXLly/H1q1bkZOTI9MeHR2N6dOn486dOyp9+B9/pullvOons78IZZ/mTk1Xbb+/n6fwhANjY2NcuHBBCDfVTa4kErstW7agX79+aNWqFZKTk/HZZ5/ho48+EpY/evQIWVlZWLlyJaZMmSK6pxrb7LFRdQl1Oj/pvKpLICIVUzjcjBkzBu+99x4cHR2hp6eH0tJSBAQECHeUlJaWNliRRI3FlStXsGzZMty/fx9mZmaYNWsWFixYICxfs2YNli9fDicnJ5l2IiJ6dRQON6tXr4a1tTVSU1NRXFwMiUQCfX194Tkm//77b4MVSdRYrFu3DuvWratxeUhICEJCQl5dQUREJEfhcCORSODj4yM8kfXbb7/FmjVrhDk3eXl5+PrrrxukSCIiIiJFvfBzbp5/CWCrVq1w8uTJly6IiIiI6GXU27ulmjVrpvTtwo2VgjeQETV6/FkmotcRX5z5jKpH9j969EjFlRDVj6rXa9T1SgsiIjHh6xeeoa6uDiMjI+EFizo6OrzdnZqsiooK/P3339DR0VHqNRNERE0d/8V7jqmpKQAIAYeoKVNTU4OZmRlDOhG9VpQKN2VlZVi+fDl8fX2FFzCKjUQiQdu2bdGmTRs8efJE1eUQvRRNTc1aXylBRCRGSoUbDQ0NfPbZZ6/FW47V1dU5T4GIiKgJUvp/6d5++20kJCQ0QClEREREL0/pOTeenp5YsGAB/vjjD9jZ2QmvX6jy7rvv1ltxRERERMpSOtxMnToVABAeHi63TCKRNPq3ghMRNQabA06ougQi0VJ6WKqioqLGz4sEmy1btsDc3Bza2tqws7NDYmJirf2jo6PRu3dv6OjooG3btvjwww9x7949pfdLRERE4vRSt1GUlJS81M5jYmIwY8YMLFy4EOnp6Rg0aBA8PT2RnZ1dbf+kpCRMnDgRfn5++PPPP/HNN9/g119/hb+//0vVQUREROKhdLgpLy/H0qVL0b59e+jp6eH69esAgEWLFmHXrl1KbSs8PBx+fn7w9/eHlZUV1q9fD6lUioiIiGr7//zzz+jUqROCgoJgbm6ON998E1OmTEFqaqqyh0FEREQipXS4Wb58OaKiorBmzRpoamoK7TY2Nti5c6fC23n8+DHS0tLg7u4u0+7u7o6UlJRq1xk4cCBu3bqF2NhYVFZW4q+//sK3336LYcOGKXsYREREJFJKh5svvvgC27dvx/jx42WeA9OrVy9cvHhR4e3k5+ejvLwcJiYmMu0mJibIy8urdp2BAwciOjoao0ePhqamJkxNTWFkZIRNmzbVuJ/S0lIUFhbKfIiIiEi8lA43t2/fRpcuXeTaKyoqXuiJvs8/Fr6ysrLGR8VfuHABQUFB+PTTT5GWloa4uDhkZWUhICCgxu2vXLkShoaGwkesT1YmIiKip5QONz169Kj2jqZvvvkGffr0UXg7xsbGUFdXl7tKc/fuXbmrOVVWrlwJR0dHzJkzB7169YKHhwe2bNmC3bt3Izc3t9p1FixYgIKCAuGTk5OjcI1ERETU9Cj9nJvFixdjwoQJuH37NioqKnDw4EFcunQJX3zxBY4cOaLwdjQ1NWFnZ4f4+HiMHDlSaI+Pj4eXl1e16zx69Eju7cZVQ2OVlZXVrqOlpQUtLS2F6yIiIqKmTekrN8OHD0dMTAxiY2MhkUjw6aefIjMzEz/88APc3NyU2lZwcDB27tyJ3bt3IzMzEzNnzkR2drYwzLRgwQJMnDhRZt8HDx5EREQErl+/juTkZAQFBcHBwQHt2rVT9lCIiIhIhJS+cgMAHh4e8PDweOmdjx49Gvfu3UNoaChyc3PRs2dPxMbGomPHjgCA3NxcmWfe+Pj4oKioCJ9//jlmzZoFIyMjvPXWW1i9evVL10JERETiIKmsaTynDqmpqcjMzIREIoGVlRXs7Ozqu7YGUVhYCENDQxQUFMDAwEDV5RA1KTZ7bFRdQp3OTzqv6hIU0hRev1Dyj/xrdhqbWTGKT4egpk2Z399KX7m5desWxo4di+TkZBgZGQEAHjx4gIEDB2L//v28G4mIVCrT0krVJSjGZbOqKyASLaXn3Pj6+uLJkyfIzMzE/fv3cf/+fWRmZqKyshJ+fn4NUSMRERGRwpS+cpOYmIiUlBR0795daOvevTs2bdoER0fHei2OiIiISFlKX7kxMzOr9mF9ZWVlaN++fb0URURERPSilA43a9aswccff4zU1FTh2TKpqamYPn061q5dW+8FEhERESlD6WEpHx8fPHr0CG+88YbwQL2ysjJoaGjA19cXvr6+Qt/79+/XX6VEREREClA63Kxfv74ByiAiIiKqH0qHm0mTJjVEHURERET1Quk5N0RERESNGcMNERERiQrDDREREYkKww0RERGJCsMNERERiYpSd0vl5ubip59+QsuWLfH2229DU1NTWFZcXIywsDB8+umn9V4k0WshxFDVFdTN3EzVFRAR1UnhKze//vorrK2tMW3aNLz//vvo2bMn/vzzT2H5w4cPsWTJkgYpkoiIiEhRCoebTz75BN7e3vjnn3/w119/wc3NDc7OzkhPT2/I+oiIiIiUovCwVFpaGjZv3gw1NTXo6+tj8+bN6NixIwYPHoxjx47BzIyXq4mIiEj1lJpzU1JSIvN97ty5UFNTg7u7O3bv3l2vhRERERG9CIXDTc+ePZGSkoJevXrJtM+ePRuVlZUYO3ZsvRdHREREpCyF59xMnDgRycnJ1S6bM2cOQkNDOTRFREREKqdwuPH398eXX35Z4/K5c+ciKyurXooiIiIielFKvxUcAM6dO4fLly9DIpGga9euckNVRERERKqiVLg5e/Ys/Pz8cOHCBVRWVgIAJBIJevTogV27dqFfv34NUiQRERGRohQelrpw4QIGDx6M5s2bY+/evfjtt9+QlpaGL7/8ElpaWhg8eDAuXLjQkLUSERER1UnhKzeLFy+Gm5sbDhw4AIlEIrT36dMHY8eOhbe3N0JCQvD11183SKFEREREilA43CQkJODo0aMywaaKRCLBJ598gqFDh9ZrcURERETKUnhYqqioCCYmJjUuNzU1RVFRUb0URURERPSiFA43nTp1wtmzZ2tc/ssvv6Bjx471UhQRERHRi1I43IwePRrBwcH4448/5JadP38es2fPxpgxY+q1OCIiIiJlKTznZsGCBTh+/DhsbW3h5uYGKysrAE/vojp+/DgcHBywYMGCBiuU6EV1mv8/VZegkBvaqq6AiEgcFA432traOHnyJNatW4f9+/fj1KlTAIBu3bph2bJlmDlzJrS0tBqsUCIiIiJFKPUQP01NTcybNw/z5s1rqHqIiIiIXorCc27++ecfbNq0CYWFhXLLCgoKalxGRERE9CopHG4+//xznD59GgYGBnLLDA0NkZiYiE2bNtVrcURERETKUjjcHDhwAAEBATUunzJlCr799tt6KYqIiIjoRSkcbq5du4auXbvWuLxr1664du1avRRFRERE9KIUDjfq6uq4c+dOjcvv3LkDNTWFN0dERETUIBROI3369MGhQ4dqXP7dd9+hT58+9VETERER0QtT+Fbwjz76CGPGjEGHDh0wdepUqKurAwDKy8uxZcsWrFu3Dvv27WuwQomIiIgUoXC4ee+99zB37lwEBQVh4cKFsLCwgEQiwbVr1/Dw4UPMmTMH77//fkPWSkRERFQnpR7it3z5cnh5eSE6OhpXr15FZWUlnJycMG7cODg4ODRUjUREREQKUyrcAICDgwODDBERETVavL2JiIiIRIXhhoiIiESF4YaIiIhEReXhZsuWLTA3N4e2tjbs7OyQmJhYa//S0lIsXLgQHTt2hJaWFjp37ozdu3e/omqJiIiosVN6QvG///6LyspK6OjoAABu3ryJ7777DtbW1nB3d1dqWzExMZgxYwa2bNkCR0dHbNu2DZ6enrhw4QLMzMyqXWfUqFH466+/sGvXLnTp0gV3795FWVmZsodBREREIqV0uPHy8oK3tzcCAgLw4MEDvPHGG2jWrBny8/MRHh6OqVOnKryt8PBw+Pn5wd/fHwCwfv16HDt2DBEREVi5cqVc/7i4OJw6dQrXr19Hy5YtAQCdOnVS9hCIiIhIxJQelvrtt98waNAgAMC3334LExMT3Lx5E1988QU2btyo8HYeP36MtLQ0uas97u7uSElJqXadw4cPw97eHmvWrEH79u3RrVs3zJ49G//++2+N+yktLUVhYaHMh4iIiMRL6Ss3jx49gr6+PgDgxx9/hLe3N9TU1NC/f3/cvHlT4e3k5+ejvLwcJiYmMu0mJibIy8urdp3r168jKSkJ2tra+O6775Cfn4/AwEDcv3+/xnk3K1euxJIlSxSui4iIiJo2pa/cdOnSBYcOHUJOTg6OHTsmXHm5e/cuDAwMlC5AIpHIfK+srJRrq1JRUQGJRILo6Gg4ODhg6NChCA8PR1RUVI1XbxYsWICCggLhk5OTo3SNRERE1HQoHW4+/fRTzJ49G506dYKDgwMGDBgA4OlVHGXeCm5sbAx1dXW5qzR3796Vu5pTpW3btmjfvj0MDQ2FNisrK1RWVuLWrVvVrqOlpQUDAwOZDxEREYmX0uHm/fffR3Z2NlJTU3Hs2DGhffDgwVi3bp3C29HU1ISdnR3i4+Nl2uPj4zFw4MBq13F0dMSdO3fw8OFDoe3y5ctQU1NDhw4dlDwSIiIiEqMXes6Nqakp9PX1ER8fLwwH9evXD5aWlkptJzg4GDt37sTu3buRmZmJmTNnIjs7GwEBAQCeDilNnDhR6D9u3Di0atUKH374IS5cuIDTp09jzpw58PX1RfPmzV/kUIiIiEhklJ5QfO/ePYwaNQonT56ERCLBlStXYGFhAX9/fxgZGSEsLEzhbY0ePRr37t1DaGgocnNz0bNnT8TGxqJjx44AgNzcXGRnZwv99fT0EB8fj48//hj29vZo1aoVRo0ahWXLlil7GERERCRSSoebmTNnolmzZsjOzoaVlZXQPnr0aMycOVOpcAMAgYGBCAwMrHZZVFSUXJulpaXcUBYRERFRFaXDzY8//ohjx47JzXHp2rWrUreCExERETUEpefcFBcXC69eeFZ+fj60tLTqpSgiIiKiF6V0uHFycsIXX3whfJdIJKioqMBnn30GV1fXei2OiIiISFlKD0t99tlncHFxQWpqKh4/foy5c+fizz//xP3795GcnNwQNRIREREpTOkrN9bW1jh37hwcHBzg5uaG4uJieHt7Iz09HZ07d26IGomIiIgUpvSVG+Dpc274viYiIiJqjF4o3Dx48ABnz57F3bt3UVFRIbPs2YfuEREREb1qSoebH374AePHj0dxcTH09fVlXnIpkUgYboiIiEillJ5zM2vWLPj6+qKoqAgPHjzAP//8I3zu37/fEDUSERERKUzpcHP79m0EBQVV+6wbIiIiIlVTOtx4eHggNTW1IWohIiIiemlKz7kZNmwY5syZgwsXLsDGxgbNmjWTWf7uu+/WW3FEREREylI63EyePBkAEBoaKrdMIpGgvLz85asiIiIiekFKh5vnb/0mIiIiakyUnnNDRERE1Ji90EP8fvrpJ/z000/VPsRv9+7d9VIYERER0YtQOtwsWbIEoaGhsLe3R9u2bWUe4kdERESkakqHm61btyIqKgoTJkxoiHqIiIiIXorSc24eP36MgQMHNkQtRERERC9N6XDj7++Pffv2NUQtRERERC9N6WGpkpISbN++HcePH0evXr3kHuIXHh5eb8URERERKUvpcHPu3DnY2toCAP744w+ZZZxcTERERKqmdLg5efJkQ9RBREREVC9e6iF+t27dwu3bt+urFiIiIqKXpnS4qaioQGhoKAwNDdGxY0eYmZnByMgIS5cu5asZiIiISOWUHpZauHAhdu3ahVWrVsHR0RGVlZVITk5GSEgISkpKsHz58oaok4iIiEghSoebPXv2YOfOnXj33XeFtt69e6N9+/YIDAxkuCEiIiKVUnpY6v79+7C0tJRrt7S0xP379+ulKCIiIqIXpXS46d27Nz7//HO59s8//xy9e/eul6KIiIiIXpTSw1Jr1qzBsGHDcPz4cQwYMAASiQQpKSnIyclBbGxsQ9RIREREpDClr9w4Ozvj8uXLGDlyJB48eID79+/D29sbly5dwqBBgxqiRiIiIiKFKX3lJjs7G1KptNqJw9nZ2TAzM6uXwoiIiIhehNJXbszNzfH333/Ltd+7dw/m5ub1UhQRERHRi1I63FRWVlb7DqmHDx9CW1u7XooiIiIielEKD0sFBwcDePpyzEWLFkFHR0dYVl5ejl9++UV4oSYRERGRqigcbtLT0wE8vXJz/vx5aGpqCss0NTXRu3dvzJ49u/4rJCIiIlKCwuGm6m3gH374ITZs2AADA4MGK4qIiIjoRSl9t1RkZGRD1EFERERUL5SeUExERETUmDHcEBERkagw3BAREZGoMNwQERGRqDDcEBERkagw3BAREZGoqDzcbNmyBebm5tDW1oadnR0SExMVWi85ORkaGhp8KjIRERHJUGm4iYmJwYwZM7Bw4UKkp6dj0KBB8PT0RHZ2dq3rFRQUYOLEiRg8ePArqpSIiIiaCpWGm/DwcPj5+cHf3x9WVlZYv349pFIpIiIial1vypQpGDduHAYMGPCKKiUiIqKmQmXh5vHjx0hLS4O7u7tMu7u7O1JSUmpcLzIyEteuXcPixYsbukQiIiJqgpR+/UJ9yc/PR3l5OUxMTGTaTUxMkJeXV+06V65cwfz585GYmAgNDcVKLy0tRWlpqfC9sLDwxYsmIiKiRk/lE4olEonM98rKSrk2ACgvL8e4ceOwZMkSdOvWTeHtr1y5EoaGhsJHKpW+dM1ERETUeKks3BgbG0NdXV3uKs3du3flruYAQFFREVJTU/HRRx9BQ0MDGhoaCA0Nxe+//w4NDQ2cOHGi2v0sWLAABQUFwicnJ6dBjoeIiIgaB5UNS2lqasLOzg7x8fEYOXKk0B4fHw8vLy+5/gYGBjh//rxM25YtW3DixAl8++23MDc3r3Y/Wlpa0NLSqt/iiYiIqNFSWbgBgODgYEyYMAH29vYYMGAAtm/fjuzsbAQEBAB4etXl9u3b+OKLL6CmpoaePXvKrN+mTRtoa2vLtRMREdHrS6XhZvTo0bh37x5CQ0ORm5uLnj17IjY2Fh07dgQA5Obm1vnMGyIiIqJnSSorKytVXcSrVFhYCENDQxQUFMDAwEDV5dAr0Gn+/1RdgkJuaI9TdQl1sjE3U3UJdfp6ZZmqS1DICZfNqi6hTiX/hKu6hDrNijmi6hLoFVHm97fK75YiIiIiqk8MN0RERCQqDDdEREQkKgw3REREJCoMN0RERCQqDDdEREQkKgw3REREJCoMN0RERCQqDDdEREQkKgw3REREJCoMN0RERCQqDDdEREQkKgw3REREJCoMN0RERCQqDDdEREQkKgw3REREJCoMN0RERCQqDDdEREQkKgw3REREJCoMN0RERCQqDDdEREQkKgw3REREJCoMN0RERCQqDDdEREQkKgw3REREJCoMN0RERCQqDDdEREQkKgw3REREJCoMN0RERCQqDDdEREQkKgw3REREJCoMN0RERCQqDDdEREQkKgw3REREJCoMN0RERCQqDDdEREQkKgw3REREJCoMN0RERCQqDDdEREQkKgw3REREJCoMN0RERCQqDDdEREQkKgw3REREJCoMN0RERCQqDDdEREQkKioPN1u2bIG5uTm0tbVhZ2eHxMTEGvsePHgQbm5uaN26NQwMDDBgwAAcO3bsFVZLREREjZ1Kw01MTAxmzJiBhQsXIj09HYMGDYKnpyeys7Or7X/69Gm4ubkhNjYWaWlpcHV1xfDhw5Genv6KKyciIqLGSqXhJjw8HH5+fvD394eVlRXWr18PqVSKiIiIavuvX78ec+fORb9+/dC1a1esWLECXbt2xQ8//PCKKyciIqLGSmXh5vHjx0hLS4O7u7tMu7u7O1JSUhTaRkVFBYqKitCyZcsa+5SWlqKwsFDmQ0REROKlsnCTn5+P8vJymJiYyLSbmJggLy9PoW2EhYWhuLgYo0aNqrHPypUrYWhoKHykUulL1U1ERESNm8onFEskEpnvlZWVcm3V2b9/P0JCQhATE4M2bdrU2G/BggUoKCgQPjk5OS9dMxERETVeGqrasbGxMdTV1eWu0ty9e1fuas7zYmJi4Ofnh2+++QZvv/12rX21tLSgpaX10vUSERFR06CyKzeampqws7NDfHy8THt8fDwGDhxY43r79++Hj48P9u3bh2HDhjV0mURERNTEqOzKDQAEBwdjwoQJsLe3x4ABA7B9+3ZkZ2cjICAAwNMhpdu3b+OLL74A8DTYTJw4ERs2bED//v2Fqz7NmzeHoaGhyo6DiIiIGg+VhpvRo0fj3r17CA0NRW5uLnr27InY2Fh07NgRAJCbmyvzzJtt27ahrKwM06ZNw7Rp04T2SZMmISoq6lWXT0RERI2QSsMNAAQGBiIwMLDaZc8HloSEhIYviIiIiJo0ld8tRURERFSfGG6IiIhIVBhuiIiISFQYboiIiEhUGG6IiIhIVBhuiIiISFQYboiIiEhUGG6IiIhIVBhuiIiISFQYboiIiEhUGG6IiIhIVBhuiIiISFQYboiIiEhUGG6IiIhIVBhuiIiISFQYboiIiEhUGG6IiIhIVBhuiIiISFQYboiIiEhUGG6IiIhIVBhuiIiISFQYboiIiEhUGG6IiIhIVBhuiIiISFQYboiIiEhUGG6IiIhIVBhuiIiISFQYboiIiEhUGG6IiIhIVBhuiIiISFQYboiIiEhUGG6IiIhIVBhuiIiISFQYboiIiEhUGG6IiIhIVBhuiIiISFQYboiIiEhUGG6IiIhIVBhuiIiISFQYboiIiEhUGG6IiIhIVBhuiIiISFQYboiIiEhUGG6IiIhIVFQebrZs2QJzc3Noa2vDzs4OiYmJtfY/deoU7OzsoK2tDQsLC2zduvUVVUpERERNgUrDTUxMDGbMmIGFCxciPT0dgwYNgqenJ7Kzs6vtn5WVhaFDh2LQoEFIT0/HJ598gqCgIBw4cOAVV05ERESNlUrDTXh4OPz8/ODv7w8rKyusX78eUqkUERER1fbfunUrzMzMsH79elhZWcHf3x++vr5Yu3btK66ciIiIGisNVe348ePHSEtLw/z582Xa3d3dkZKSUu06Z86cgbu7u0ybh4cHdu3ahSdPnqBZs2Zy65SWlqK0tFT4XlBQAAAoLCx82UOgJqKi9JGqS1BIoaRS1SXUqfzfclWXUKeH5Y2/RgD493GxqkuoU+mTJ6ouoU78t/z1UfXfurKy7n8rVRZu8vPzUV5eDhMTE5l2ExMT5OXlVbtOXl5etf3LysqQn5+Ptm3byq2zcuVKLFmyRK5dKpW+RPVE9c9Q1QUoJFPVBdTJQdUFKOrqu6quQBT++13T+JtD9aeoqAiGhrX/d1dZuKkikUhkvldWVsq11dW/uvYqCxYsQHBwsPC9oqIC9+/fR6tWrWrdDxE1PYWFhZBKpcjJyYGBgYGqyyGielRZWYmioiK0a9euzr4qCzfGxsZQV1eXu0pz9+5duaszVUxNTavtr6GhgVatWlW7jpaWFrS0tGTajIyMXrxwImr0DAwMGG6IRKiuKzZVVDahWFNTE3Z2doiPj5dpj4+Px8CBA6tdZ8CAAXL9f/zxR9jb21c734aIiIhePyq9Wyo4OBg7d+7E7t27kZmZiZkzZyI7OxsBAQEAng4pTZw4UegfEBCAmzdvIjg4GJmZmdi9ezd27dqF2bNnq+oQiIiIqJFR6Zyb0aNH4969ewgNDUVubi569uyJ2NhYdOzYEQCQm5sr88wbc3NzxMbGYubMmdi8eTPatWuHjRs34r333lPVIRBRI6KlpYXFixfLDUUT0etFUqnIPVVERERETYTKX79AREREVJ8YboiIiEhUGG6IiIhIVBhuiIiISFQYboiIiEhUVP76BSKiF3Xr1i1EREQgJSUFeXl5kEgkMDExwcCBAxEQEMB3yBG9pngrOBE1SUlJSfD09IRUKoW7uztMTExQWVmJu3fvIj4+Hjk5OTh69CgcHR1VXSoRvWIMN0TUJPXr1w9vvvkm1q1bV+3ymTNnIikpCb/++usrroyIVI3hhoiapObNmyMjIwPdu3evdvnFixfRp08f/Pvvv6+4MiJSNU4oJqImqW3btkhJSalx+ZkzZ9C2bdtXWBERNRacUExETdLs2bMREBCAtLQ0uLm5wcTEBBKJBHl5eYiPj8fOnTuxfv16VZdJRCrAYSkiarJiYmKwbt06pKWloby8HACgrq4OOzs7BAcHY9SoUSqukIhUgeGGiJq8J0+eID8/HwBgbGyMZs2aqbgiIlIlhhsiIiISFU4oJiIiIlFhuCEiIiJRYbghIiIiUWG4ISIiIlFhuCEiIiJRYbghIiIiUWG4ISIiIlFhuCEiIiJR+X/WFfAMD15BzQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart = e.plot(kind='bar')\n",
    "chart.set_ylabel('tonnes CO2 per £')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 4.2: Calculating eL\n",
    "\n",
    "Try ```eL = np.dot(e, L)```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.91, 2.6 , 2.04, 2.12, 1.78, 1.93]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eL = np.dot(e, L)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want to make a bar chart similar to the one above but for eL we need eL to be a dataframe. But when we use eL for later calculations it needs to stay as an array. Try:\n",
    "\n",
    "```python\n",
    "eLdataframe = pd.DataFrame(eL,columns=sectors)\n",
    "chart = eLdataframe.plot( kind = 'bar')\n",
    "chart.set_ylabel('tonnes CO2 per £')\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'tonnes CO2 per £')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eLdataframe = pd.DataFrame(eL,columns=sectors)\n",
    "chart = eLdataframe.plot( kind = 'bar')\n",
    "chart.set_ylabel('tonnes CO2 per £')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's think a bit more about what $\\textbf{eL}$ represents. We have multiplied emissions intensity of <b>industries</b> by the matrix which translates industry inputs to products. \n",
    "\n",
    "$\\textbf{eL}$ is therefore the emissions intensity of <b>products</b>\n",
    "\n",
    "Each number represents the full supply chain emissions per unit spend of a product.\n",
    "\n",
    "Compare the figures you have for the emissions intensity of an industry with the corresponding product emissions intensity. You should find that they are larger because we are taking account of the full supply chain of production.\n",
    "\n",
    "Observe that for food, the product emissions intensity is now larger than the product emissions intensity for energy. This is because we are including emissions from highly polluting sectors like energy in the supply chain of a less intensive product like food."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 4.3: Calculating Total town level footprints\n",
    "\n",
    "$\\textbf{q = eLY}$. \n",
    "\n",
    "We calculated ```eL``` in the previous exercise. Now we need to multiply it by ```Y```. \n",
    "\n",
    "Try ```q = np.dot(eL, Y)```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4443.62, 2386.38]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q = np.dot(eL, Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should find that you have a result matrix of dimension [$1$,$2$]. But what do the numbers mean?\n",
    "\n",
    "Lets look at the dimensions of $\\textbf{e}$, $\\textbf{L}$ and $\\textbf{Y}$:\n",
    "\n",
    "[$1$,$6$]  [$6$,$6$]  [$6$,$2$]\n",
    "\n",
    "The final result is the emissions by final demand region [$1$,$2$] where the $1$ is from the emissions intensity vector $\\textbf{e}$  and the $2$ is from the final demand matrix $\\textbf{Y}$. So the emissions from Eagleton's consumption are 4443.62 tonnes $CO2$ or the carbon footprint of Eagleton is 4443.62 tonnes $CO2$.\n",
    "\n",
    "If this reasoning is correct, total emissions should be preserved. Can you check if the sum of the production emissions $\\textbf{f}$ is the same as the sum of the consumption emissions $\\textbf{q}$ that you have just calculated?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(q,1)-np.sum(f,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can make your result into a neat DataFrame like this: \n",
    "\n",
    "```total_country_footprints = pd.DataFrame(q,columns=final_demand)```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Eagleton Final Demand</th>\n",
       "      <th>Pawnee Final Demand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4443.623861</td>\n",
       "      <td>2386.376139</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Eagleton Final Demand  Pawnee Final Demand\n",
       "0            4443.623861          2386.376139"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_country_footprints = pd.DataFrame(q,columns=final_demand)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 4.4: Calculating product level footprints\n",
    "\n",
    "If we wanted to breakdown the footprint of each country by product we would need to be pre-multiplying $\\textbf{Y}$ by something with 6 product rows rather than the 1 row from eL that we used in Excercise 4.2.\n",
    "\n",
    "If we diagonalise $\\textbf{eL}$, we will make a matrix of products by products. The multiplying this by $\\textbf{Y}$ gives 'products by regions'\n",
    "\n",
    "Try multiplying a diagonalised $\\textbf{eL}$ by $\\textbf{Y}$ to make ```country_footprints_by_product```.\n",
    "\n",
    "Use the function ```np.diagflat(eL)``` then multiply by $\\textbf{Y}$:\n",
    "\n",
    "```python\n",
    "country_footprints_by_product = np.dot(np.diagflat(eL), Y)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2005.06,  174.35],\n",
       "       [ 987.92,   52.  ],\n",
       "       [ 672.28,   30.56],\n",
       "       [ 424.93, 1232.28],\n",
       "       [ 160.02,  355.61],\n",
       "       [ 193.42,  541.58]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_footprints_by_product = np.dot(np.diagflat(eL), Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make your result into a DataFrame called ```country_footprints_by_product```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Eagleton Final Demand</th>\n",
       "      <th>Pawnee Final Demand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Eagleton Food</th>\n",
       "      <td>2005.055530</td>\n",
       "      <td>174.352655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eagleton Machines</th>\n",
       "      <td>987.922098</td>\n",
       "      <td>51.995900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eagleton Energy</th>\n",
       "      <td>672.276709</td>\n",
       "      <td>30.558032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pawnee Food</th>\n",
       "      <td>424.925623</td>\n",
       "      <td>1232.284308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pawnee Machines</th>\n",
       "      <td>160.022905</td>\n",
       "      <td>355.606456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pawnee Energy</th>\n",
       "      <td>193.420995</td>\n",
       "      <td>541.578787</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Eagleton Final Demand  Pawnee Final Demand\n",
       "Eagleton Food                2005.055530           174.352655\n",
       "Eagleton Machines             987.922098            51.995900\n",
       "Eagleton Energy               672.276709            30.558032\n",
       "Pawnee Food                   424.925623          1232.284308\n",
       "Pawnee Machines               160.022905           355.606456\n",
       "Pawnee Energy                 193.420995           541.578787"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_footprints_by_product = pd.DataFrame(country_footprints_by_product,index = sectors, columns=final_demand)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Try:\n",
    "\n",
    "```python\n",
    "chart = country_footprints_by_product.plot(kind='bar')\n",
    "chart.set_ylabel('tonnes CO2 per £')\n",
    "```\n",
    "\n",
    "and\n",
    "\n",
    "```python\n",
    "chart = country_footprints_by_product.plot(kind='bar', stacked=True)\n",
    "chart.set_ylabel('tonnes CO2 per £')\n",
    "```\n",
    "\n",
    "and\n",
    "\n",
    "```python\n",
    "dataframe = country_footprints_by_product.transpose(copy=True)\n",
    "plot = dataframe.plot(kind='bar', stacked=True)\n",
    "plot.set_ylabel('tonnes CO2 per £')\n",
    "```\n",
    "\n",
    "and\n",
    "\n",
    "```python\n",
    "plots = country_footprints_by_product.plot(kind='barh', subplots=True)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<Axes: title={'center': 'Eagleton Final Demand'}>,\n",
       "       <Axes: title={'center': 'Pawnee Final Demand'}>], dtype=object)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart = country_footprints_by_product.plot(kind='bar')\n",
    "chart.set_ylabel('tonnes CO2 per £')\n",
    "\n",
    "chart = country_footprints_by_product.plot(kind='bar', stacked=True)\n",
    "chart.set_ylabel('tonnes CO2 per £')\n",
    "\n",
    "dataframe = country_footprints_by_product.transpose(copy=True)\n",
    "plot = dataframe.plot(kind='bar', stacked=True)\n",
    "plot.set_ylabel('tonnes CO2 per £')\n",
    "\n",
    "plots = country_footprints_by_product.plot(kind='barh', subplots=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 4.5 Calculating emissions by source industry and end product\n",
    "\n",
    "First make a new variable ```y_eagleton``` which extracts Eagleton's final demand column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Eagleton Food        690\n",
       "Eagleton Machines    380\n",
       "Eagleton Energy      330\n",
       "Pawnee Food          200\n",
       "Pawnee Machines       90\n",
       "Pawnee Energy        100\n",
       "Name: Eagleton Final Demand, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_eagleton = Y['Eagleton Final Demand']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do the same for Pawnee and make ```y_pawnee```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Eagleton Food         60\n",
       "Eagleton Machines     20\n",
       "Eagleton Energy       15\n",
       "Pawnee Food          580\n",
       "Pawnee Machines      200\n",
       "Pawnee Energy        280\n",
       "Name: Pawnee Final Demand, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pawnee = Y['Pawnee Final Demand']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now want to make two separate footprint result matrices. One for Eagleton and one for Pawnee. In each matrix, we want the columns to represent final products and the rows to be source industries.\n",
    "\n",
    "Using these matrices we can sum the columns to find the product footprints as shown in <b>Exercise 4.4</b>. But more interestingly, we can look at the composition of industries (and associated emissions) that are used to make a product.\n",
    "\n",
    "To do this, we will need to ensure that our multiplication gives a result of dimension $[6,6]$ \n",
    "\n",
    "The first matrix needs to be an 'industry by industry' matrix\n",
    "\n",
    "$\\textbf{L}$ is 'industry by product' matrix\n",
    "\n",
    "then the final matrix is a 'product by product' matrix\n",
    "\n",
    "The best way to do this is to diagonalise both ```e``` and ```y_eagleton```\n",
    "\n",
    "try: \n",
    "```python\n",
    "eagleton_footprint_full = np.dot(np.dot(np.diagflat(e.values), L), np.diag(y_eagleton))\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[8.59e+02, 6.70e+00, 1.67e+00, 9.17e+00, 3.19e-01, 2.47e-01],\n",
       "       [3.47e+02, 4.67e+02, 4.36e+01, 1.35e+01, 4.91e+00, 4.25e+00],\n",
       "       [3.67e+02, 2.22e+02, 3.97e+02, 1.89e+01, 8.01e+00, 1.38e+01],\n",
       "       [4.71e+01, 5.33e+00, 2.86e+00, 2.38e+02, 2.47e+00, 5.95e-01],\n",
       "       [1.94e+02, 1.62e+02, 9.70e+01, 5.24e+01, 1.11e+02, 1.47e+01],\n",
       "       [1.90e+02, 1.24e+02, 1.30e+02, 9.25e+01, 3.36e+01, 1.60e+02]])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eagleton_footprint_full = np.dot(np.dot(np.diagflat(e.values), L), np.diag(y_eagleton))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is actually a $[6,6]$ array of Eagleton's total footprint. The columns are the products and the rows are the breakdown of the supply chain - what industries are involved in making the product.\n",
    "\n",
    "You'll notice that you used ```np.diag(y_eagleton)``` rather than ```np.diagflat(y_eagleton)```. The reason for this is because when you select a single column from a matrix, python sees this as having zero width, just length and we have to use a different function. \n",
    "\n",
    "You'll also notice that we are refering to ```e.values``` before we diagonalise it. This is simply to make the calculation work with elements that are all arrays (rather than dataframes).\n",
    "\n",
    "It's annoying but the above code should work for you. \n",
    "\n",
    "Now make ```eagleton_footprint_full``` into a dataframe:\n",
    "\n",
    "```python\n",
    "eagleton_footprint_full = pd.DataFrame(eagleton_footprint_full, index=sectors, columns=sectors)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Eagleton Food</th>\n",
       "      <th>Eagleton Machines</th>\n",
       "      <th>Eagleton Energy</th>\n",
       "      <th>Pawnee Food</th>\n",
       "      <th>Pawnee Machines</th>\n",
       "      <th>Pawnee Energy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Eagleton Food</th>\n",
       "      <td>858.795113</td>\n",
       "      <td>6.704774</td>\n",
       "      <td>1.673607</td>\n",
       "      <td>9.167497</td>\n",
       "      <td>0.319417</td>\n",
       "      <td>0.246643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eagleton Machines</th>\n",
       "      <td>347.366676</td>\n",
       "      <td>467.464696</td>\n",
       "      <td>43.644197</td>\n",
       "      <td>13.529921</td>\n",
       "      <td>4.905354</td>\n",
       "      <td>4.252255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eagleton Energy</th>\n",
       "      <td>367.461070</td>\n",
       "      <td>222.124671</td>\n",
       "      <td>396.910395</td>\n",
       "      <td>18.872673</td>\n",
       "      <td>8.006738</td>\n",
       "      <td>13.793595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pawnee Food</th>\n",
       "      <td>47.134515</td>\n",
       "      <td>5.328787</td>\n",
       "      <td>2.861154</td>\n",
       "      <td>238.448912</td>\n",
       "      <td>2.468680</td>\n",
       "      <td>0.594996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pawnee Machines</th>\n",
       "      <td>194.120382</td>\n",
       "      <td>162.194593</td>\n",
       "      <td>96.982079</td>\n",
       "      <td>52.415932</td>\n",
       "      <td>110.723850</td>\n",
       "      <td>14.652393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pawnee Energy</th>\n",
       "      <td>190.177775</td>\n",
       "      <td>124.104578</td>\n",
       "      <td>130.205276</td>\n",
       "      <td>92.490689</td>\n",
       "      <td>33.598866</td>\n",
       "      <td>159.881113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Eagleton Food  Eagleton Machines  Eagleton Energy  \\\n",
       "Eagleton Food         858.795113           6.704774         1.673607   \n",
       "Eagleton Machines     347.366676         467.464696        43.644197   \n",
       "Eagleton Energy       367.461070         222.124671       396.910395   \n",
       "Pawnee Food            47.134515           5.328787         2.861154   \n",
       "Pawnee Machines       194.120382         162.194593        96.982079   \n",
       "Pawnee Energy         190.177775         124.104578       130.205276   \n",
       "\n",
       "                   Pawnee Food  Pawnee Machines  Pawnee Energy  \n",
       "Eagleton Food         9.167497         0.319417       0.246643  \n",
       "Eagleton Machines    13.529921         4.905354       4.252255  \n",
       "Eagleton Energy      18.872673         8.006738      13.793595  \n",
       "Pawnee Food         238.448912         2.468680       0.594996  \n",
       "Pawnee Machines      52.415932       110.723850      14.652393  \n",
       "Pawnee Energy        92.490689        33.598866     159.881113  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eagleton_footprint_full = pd.DataFrame(eagleton_footprint_full, index=sectors, columns=sectors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 4.6: What does this result matrix mean?\n",
    "\n",
    "What happens if you sum down the columns of ```eagleton_footprint_full```, remember to sum down the columns, use ```0``` as the dimension with ```np.sum```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Eagleton Food        2005.055530\n",
       "Eagleton Machines     987.922098\n",
       "Eagleton Energy       672.276709\n",
       "Pawnee Food           424.925623\n",
       "Pawnee Machines       160.022905\n",
       "Pawnee Energy         193.420995\n",
       "dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(eagleton_footprint_full,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Where have you seen these numbers before? Scroll up and check <b>Exercise 4.4</b>. You should notice that the column sum is the product footprint.\n",
    "\n",
    "Try \n",
    "```python\n",
    "eagleton_footprint_full.loc['Pawnee Energy','Eagleton Machines']\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "124.10"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eagleton_footprint_full.loc['Pawnee Energy','Eagleton Machines']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This refers to the 124 tonnes of GHGs that are burnt making the Pawnee Energy that is part of the supply chain of the Eagleton Machines that are purchased as final demand by the people of Eagleton!\n",
    "\n",
    "Complicated?\n",
    "\n",
    "Yes\n",
    "\n",
    "Can you write a single line of code to make a dataframe of Pawnee's emissions by source industry and end product? Call the result ```pawnee_footprint_full```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Eagleton Food</th>\n",
       "      <th>Eagleton Machines</th>\n",
       "      <th>Eagleton Energy</th>\n",
       "      <th>Pawnee Food</th>\n",
       "      <th>Pawnee Machines</th>\n",
       "      <th>Pawnee Energy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Eagleton Food</th>\n",
       "      <td>74.677836</td>\n",
       "      <td>0.352883</td>\n",
       "      <td>0.076073</td>\n",
       "      <td>26.585741</td>\n",
       "      <td>0.709816</td>\n",
       "      <td>0.690601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eagleton Machines</th>\n",
       "      <td>30.205798</td>\n",
       "      <td>24.603405</td>\n",
       "      <td>1.983827</td>\n",
       "      <td>39.236771</td>\n",
       "      <td>10.900786</td>\n",
       "      <td>11.906314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eagleton Energy</th>\n",
       "      <td>31.953136</td>\n",
       "      <td>11.690772</td>\n",
       "      <td>18.041382</td>\n",
       "      <td>54.730752</td>\n",
       "      <td>17.792751</td>\n",
       "      <td>38.622065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pawnee Food</th>\n",
       "      <td>4.098653</td>\n",
       "      <td>0.280462</td>\n",
       "      <td>0.130052</td>\n",
       "      <td>691.501844</td>\n",
       "      <td>5.485956</td>\n",
       "      <td>1.665989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pawnee Machines</th>\n",
       "      <td>16.880033</td>\n",
       "      <td>8.536558</td>\n",
       "      <td>4.408276</td>\n",
       "      <td>152.006202</td>\n",
       "      <td>246.053000</td>\n",
       "      <td>41.026701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pawnee Energy</th>\n",
       "      <td>16.537198</td>\n",
       "      <td>6.531820</td>\n",
       "      <td>5.918422</td>\n",
       "      <td>268.222999</td>\n",
       "      <td>74.664147</td>\n",
       "      <td>447.667118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Eagleton Food  Eagleton Machines  Eagleton Energy  \\\n",
       "Eagleton Food          74.677836           0.352883         0.076073   \n",
       "Eagleton Machines      30.205798          24.603405         1.983827   \n",
       "Eagleton Energy        31.953136          11.690772        18.041382   \n",
       "Pawnee Food             4.098653           0.280462         0.130052   \n",
       "Pawnee Machines        16.880033           8.536558         4.408276   \n",
       "Pawnee Energy          16.537198           6.531820         5.918422   \n",
       "\n",
       "                   Pawnee Food  Pawnee Machines  Pawnee Energy  \n",
       "Eagleton Food        26.585741         0.709816       0.690601  \n",
       "Eagleton Machines    39.236771        10.900786      11.906314  \n",
       "Eagleton Energy      54.730752        17.792751      38.622065  \n",
       "Pawnee Food         691.501844         5.485956       1.665989  \n",
       "Pawnee Machines     152.006202       246.053000      41.026701  \n",
       "Pawnee Energy       268.222999        74.664147     447.667118  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pawnee_footprint_full = pd.DataFrame(np.dot(np.dot(np.diagflat(e.values), L), np.diag(y_pawnee)),index=sectors, columns=sectors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What happens if you find the row sum of both ```eagleton_footprint_full``` and ```pawnee_footprint_full```? Try\n",
    "\n",
    "```python\n",
    "np.sum(eagleton_footprint_full,1)+np.sum(pawnee_footprint_full,1)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Eagleton Food         980.0\n",
       "Eagleton Machines    1000.0\n",
       "Eagleton Energy      1200.0\n",
       "Pawnee Food          1000.0\n",
       "Pawnee Machines      1100.0\n",
       "Pawnee Energy        1550.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(eagleton_footprint_full,1)+np.sum(pawnee_footprint_full,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do these numbers look familiar? \n",
    "\n",
    "You should find that this is our original $\\textbf{f}$ the vector of emissions from production. \n",
    "\n",
    "Production emissions get distributed across the rows. \n",
    "\n",
    "Or in other words, you can see the contribution that each industry makes to the production of each product (column)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 4.7: Graphing consumption emissions by source region\n",
    "\n",
    "Let's create a dataframe that tells us the emissions from Eagleton's consumption that are sourced domestically and those that are sourced from Pawnee. And the emissions from Pawnee's consumption that are sourced domestically and those that are sourced from Eagleton.\n",
    "\n",
    "Your finished table is going to look like this:\n",
    "\n",
    "<img src=\"https://github.com/earao/images/blob/main/Screenshot%202023-12-08%20at%2020.23.26.png?raw=true\" width=300 height=120 />\n",
    "\n",
    "First we make a $2$ by $2$ array of zeros to hold the data:\n",
    "\n",
    "```python\n",
    "data = np.zeros((2,2))\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0.],\n",
       "       [0., 0.]])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.zeros((2,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets make our row headings:\n",
    "\n",
    "```python\n",
    "index = ['Eagleton CBA', 'Pawnee CBA']\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Eagleton CBA', 'Pawnee CBA']"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = ['Eagleton CBA', 'Pawnee CBA']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the column headings:\n",
    "\n",
    "```python\n",
    "columns = ['domestic', 'imports']\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['domestic', 'imports']"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = ['domestic', 'imports']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we sum the emissions from Eagleton' consumption that are sourced domestically and place this in the upper left cell of the data array. These are the emissions in ```eagleton_footprint_full``` that occur in the <b>rows</b> that represent Eagleton's industries. Try:\n",
    "\n",
    "```python\n",
    "data[0,0] = np.sum(np.sum(eagleton_footprint_full.loc['Eagleton Food':'Eagleton Energy',:],0),0)\n",
    "data\n",
    "```\n",
    "it's ok to have two lines of code in one box."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2785.24,    0.  ],\n",
       "       [   0.  ,    0.  ]])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0,0] = np.sum(np.sum(eagleton_footprint_full.loc['Eagleton Food':'Eagleton Energy',:],0),0)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can you write code for the top right to calculate the emissions from Eagleton's consumption that are imported?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2785.24, 1658.38],\n",
       "       [   0.  ,    0.  ]])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0,1] = np.sum(np.sum(eagleton_footprint_full.loc['Pawnee Food':'Pawnee Energy',:],0),0)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now write two further lines of code to fill in the bottom row. Make sure the bottom left is the emissions sourced from Pawnee that are consumed by Pawnee! \n",
    "\n",
    "Remember to use your other 'footprint_full' dataframe for Pawnee's total footprint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2785.24, 1658.38],\n",
       "       [1991.62,    0.  ]])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[1,0] = np.sum(np.sum(pawnee_footprint_full.loc['Pawnee Food':'Pawnee Energy',:],0),0)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2785.24, 1658.38],\n",
       "       [1991.62,  394.76]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[1,1] = np.sum(np.sum(pawnee_footprint_full.loc['Eagleton Food':'Eagleton Energy',:],0),0)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now put it all together in your dataframe. Call the dataframe ```all_data```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>domestic</th>\n",
       "      <th>imports</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Eagleton CBA</th>\n",
       "      <td>2785.239291</td>\n",
       "      <td>1658.384570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pawnee CBA</th>\n",
       "      <td>1991.615430</td>\n",
       "      <td>394.760709</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 domestic      imports\n",
       "Eagleton CBA  2785.239291  1658.384570\n",
       "Pawnee CBA    1991.615430   394.760709"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data = pd.DataFrame(data,index = index, columns = columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a stacked bar chart showing Eagleton and Pawnee's footprint by domestic and imported emissions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'tonnes CO2')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart = all_data.plot(kind='bar', stacked=True)\n",
    "chart.set_ylabel('tonnes CO2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should have got a graph that looks like this:\n",
    "\n",
    "<img src=\"https://github.com/earao/images/blob/main/Screenshot%202023-12-08%20at%2020.23.45.png?raw=true\" width=500 height=400/>\n",
    "\n",
    "Can you see that Eagleton is reliant on imports, whereas Pawnee is a town which is more sufficient. Think about how you might expect the footprints of a developed versus developing country to look if you broke the emissions down by domestic and imports.\n",
    "\n",
    "## Key learning points\n",
    "\n",
    "You should have learnt:\n",
    "\n",
    "<ol>\n",
    "<li>How to make the individual matrix elements representing the transactions matrix, final demand and emissions</li>\n",
    "<li>How to make dataframes</li>\n",
    "<li>How to calculate e and L</li>\n",
    "<li>How to calculate total footprints, footprints by product and footprints by source industry and end product</li>\n",
    "<li>How to make bar charts and stacked bar charts of your results</li>\n",
    "   \n",
    "</ol>\n",
    "\n"
   ]
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